{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 15 – Autoencoders - 自编码器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自编码器是能够在**无监督**的情况下学习输入数据的有效表示（叫做**编码**）的人工神经网络（即，训练集是未标记）。\n",
    "* 这些编码通常具有比输入数据低得多的维度，使得自编码器对降维有用（参见第 8 章）。\n",
    "* 更重要的是，自编码器可以作为强大的特征检测器，它们可以用于无监督的深度神经网络预训练（正如我们在第 11 章中讨论过的）。\n",
    "* 最后，他们能够随机生成与训练数据非常相似的新数据；这被称为**生成模型**。例如，你可以在脸部图片上训练自编码器，然后可以生成新脸部。\n",
    "\n",
    "令人惊讶的是，自编码器只需学习将输入复制到其输出即可工作。 这听起来像是一件小事，但我们会看到以各种方式约束网络可能会让它变得相当困难。例如，你可以限制内部表示的大小，或者可以向输入添加噪声并训练网络以恢复原始输入。这些约束防止自编码器将输入直接复制到输出，这迫使它学习表示数据的有效方法。 简而言之，编码是自编码器在某些限制条件下尝试学习身份函数的副产品。\n",
    "\n",
    "在本章中，我们将更深入地\n",
    "* 解释自编码器如何工作，\n",
    "* 可以施加什么类型的约束\n",
    "* 以及如何使用 TensorFlow 实现它们，无论是用来降维，特征提取，无监督预训练还是作为生成式模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "def reset_graph(seed=42):\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \"F:\\ML\\Machine learning\\Hands-on machine learning with scikit-learn and tensorflow\"\n",
    "CHAPTER_ID = \"15_autoencoders\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一对实用程序用于绘制灰度28x28图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(image, shape=[28, 28]):\n",
    "    plt.imshow(image.reshape(shape), cmap=\"Greys\", interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multiple_images(images, n_rows, n_cols, pad=2):\n",
    "    images = images - images.min()  # make the minimum == 0, so the padding looks white\n",
    "    w,h = images.shape[1:]\n",
    "    image = np.zeros(((w+pad)*n_rows+pad, (h+pad)*n_cols+pad))\n",
    "    for y in range(n_rows):\n",
    "        for x in range(n_cols):\n",
    "            image[(y*(h+pad)+pad):(y*(h+pad)+pad+h),(x*(w+pad)+pad):(x*(w+pad)+pad+w)] = images[y*n_cols+x]\n",
    "    plt.imshow(image, cmap=\"Greys\", interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Efficient Data Representations - 高效的数据表示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你发现以下哪一个数字序列最容易记忆？\n",
    "\n",
    "* 40, 27, 25, 36, 81, 57, 10, 73, 19, 68\n",
    "* 50, 25, 76, 38, 19, 58, 29, 88, 44, 22, 11, 34, 17, 52, 26, 13, 40, 20\n",
    "\n",
    "乍一看，第一个序列似乎应该更容易，因为它要短得多。 但是，如果仔细观察第二个序列，则可能会注意到它遵循两条简单规则：**偶数是前面数的一半，奇数是前面数的三倍加一（**这是一个着名的序列，称为冰雹序列）。\n",
    "\n",
    "一旦你注意到这种模式，第二个序列比第一个更容易记忆，因为你只需要记住两个规则，第一个数字和序列的长度。 请注意，如果你可以快速轻松地记住非常长的序列，则你不会在意第二个序列中存在的模式。 你只需要了解每一个数字，就是这样。 事实上，很难记住长序列，因此**识别模式**非常有用，并且希望能够澄清为什么在训练过程中限制自编码器会促使它发现并利用数据中的模式。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "记忆，感知和模式匹配之间的关系在 20 世纪 70 年代早期由 William Chase 和 Herbert Simon 进行过着名的研究。 他们观察到，专家棋手能够通过观看棋盘5秒钟来记忆所有棋子的位置，这是大多数人认为不可能完成的任务。 然而，只有当这些棋子被放置在现实位置（来自实际比赛）时才是这种情况，而不是随机放置棋子。 国际象棋专家没有比你更好的记忆，他们只是更容易看到国际象棋模式，这要归功于他们对比赛的经验。 注意模式有助于他们有效地存储信息。\n",
    "\n",
    "就像这个记忆实验中的象棋棋手一样，一个自编码器会查看输入信息，将它们转换为高效的内部表示形式，然后吐出一些（希望）看起来非常接近输入的东西。 自编码器总是由两部分组成：\n",
    "* 将输入转换为内部表示的编码器（或识别网络），\n",
    "* 然后是将内部表示转换为输出的解码器（或生成网络）（见图 15-1）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如你所见，自编码器通常具有与**多层感知器**（MLP，请参阅第 10 章）相同的体系结构，**但输出层中的神经元数量必须等于输入数量**。 在这个例子中，只有一个由两个神经元（编码器）组成的隐藏层和一个由三个神经元（解码器）组成的输出层。 由于自编码器试图重构输入，所以输出通常被称为**重建**，并且损失函数包含**重建损失**，当重建与输入不同时，重建损失会对模型进行惩罚"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于内部表示具有比输入数据更低的维度（它是 2D 而不是 3D），所以自编码器被认为是**不完整的**。 不完整的自编码器不能简单地将其输入复制到编码，但它必须找到一种方法来输出其输入的副本。 它被迫学习输入数据中最重要的特征（并删除不重要的特征）。\n",
    "\n",
    "我们来看看如何实现一个非常简单的不完整的自编码器，以降低维度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Performing PCA with an Undercomplete Linear Autoencoder - 使用欠完全线性自动编码器执行PCA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建3D数据集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.random as rnd\n",
    "\n",
    "rnd.seed(4)\n",
    "m = 200\n",
    "w1, w2 = 0.1, 0.3\n",
    "noise = 0.1\n",
    "\n",
    "angles = rnd.rand(m) * 3 * np.pi / 2 - 0.5\n",
    "data = np.empty((m, 3))\n",
    "data[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\n",
    "data[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\n",
    "data[:, 2] = data[:, 0] * w1 + data[:, 1] * w2 + noise * rnd.randn(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "规范化数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(data[:100])\n",
    "X_test = scaler.transform(data[100:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在让我们构建Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果自动编码器仅使用线性激活，则成本函数为均方误差（MSE），然后可以证明它最终执行**主成分分析**（见第8章）。\n",
    "\n",
    "以下代码构建一个简单的线性自动编码器,在3D数据集上执行PCA，将其投影到2D："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：我们现在使用`tf.layers模块中的dense（）`函数，而不是使用`tensorflow.contrib.layers`模块中的`fully_connected（）`函数，该函数在编写本章时不存在。这是首选，因为`contrib`中的任何内容都可能更改或删除，恕不另行通知，而`tf.layers`是官方API的一部分。正如你将看到的，代码大致相同。\n",
    "\n",
    "与本章相关的主要差异是：\n",
    "\n",
    "* `scope` 参数已重命名为 `name`, 在具有该参数的所有参数中删除了`_fn`后缀 (例如， `activation_fn` 参数已重命名为`activation`).\n",
    "* `weights`参数重命名为 `kernel` ,权重变量现在命名为 `\"kernel\"` 而不是 `\"weights\"`,\n",
    "* the bias variable 现在被命名为`\"bias\"` 而不是 `\"biases\"`,\n",
    "* 默认 activation is `None` 而不是 `tf.nn.relu`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "reset_graph()\n",
    "\n",
    "n_inputs = 3 # 3D输入\n",
    "n_hidden = 2  # 二维编码\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "hidden = tf.layers.dense(X, n_hidden)\n",
    "outputs = tf.layers.dense(hidden, n_outputs)\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(reconstruction_loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这段代码与我们在过去章节中构建的所有MLP实际上没有太大差别。需要注意的两件事是：\n",
    "* 输出数等于输入数。\n",
    "* 为了执行简单的PCA，我们设置`activation_fn = None`（即，所有神经元都是线性的），并且成本函数是`MSE`。 我们很快就会看到更复杂的自动编码器。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在让我们加载数据集，在训练集上训练模型，并用它来编码测试集（即将其投影到2D）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)  \n",
    "sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))  \n",
    "\n",
    "n_iterations = 1000\n",
    "codings = hidden # 隐藏层的输出提供编码\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        training_op.run(feed_dict={X: X_train}) # no labels（无监督）\n",
    "    codings_val = codings.eval(feed_dict={X: X_test})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果出现以下类似错误：\n",
    "\n",
    "`Blas GEMM launch failed : a.shape=(100, 3), b.shape=(3, 2), m=100, n=2, k=3`\n",
    "\n",
    "这是调用GPU时，显存分配遇到了问题，比较保险的方式是在模型训练之前为tensorflow或者keras分配显存空间。[解决办法:](https://www.jianshu.com/p/72c4403253aa)tensorflow就用如下语句创建session：\n",
    "```python\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)  \n",
    "sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))  \n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure linear_autoencoder_pca_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04d112668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,3))\n",
    "plt.plot(codings_val[:,0], codings_val[:, 1], \"b.\")\n",
    "plt.xlabel(\"$z_1$\", fontsize=18)\n",
    "plt.ylabel(\"$z_2$\", fontsize=18, rotation=0)\n",
    "save_fig(\"linear_autoencoder_pca_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图15-2显示了原始3D数据集（在左侧）和自动编码器的隐藏层（即右侧的编码层）的输出。正如你所看到的，自动编码器找到了最好的2D平面来投影数据，保留尽可能多的数据方差（就像PCA一样）"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Stacked Autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就像我们讨论的其他神经网络一样，自动编码器可以有多个隐藏层。在这种情况下，它们被称为**堆叠自动编码器**（或深度自动编码器）。添加更多层有助于自动编码器学习更复杂的编码。但是，必须注意不要使自动编码器太强大。\n",
    "\n",
    "想象一个编码器如此强大，它只是学会将每个输入映射到一个任意数字（并且解码器学习反向映射）。显然，这样的自动编码器将完美地重建训练数据，但是它不会在过程中学习任何有用的数据表示（并且它不可能很好地适用于新实例）。\n",
    "\n",
    "堆叠自动编码器的架构通常是**关于中央隐藏层（编码层）对称**的。简单来说，它看起来像一个三明治。例如，MNIST的自动编码器（在第3章中介绍）可能有784个输入，其次是隐藏层有300个神经元，然后是150个神经元的中央隐藏层，然后是另一个隐藏层有300个神经元，输出层有784个神经元。该堆叠自动编码器如图15-3所示。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1  TensorFlow Implementation - TensorFlow 实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可以实现堆叠自动编码器，就像常规深度MLP一样。特别是，我们可以应用第11章中用于训练深度网络的相同技术。 例如，以下代码\n",
    "* 为MNIST构建堆叠自动编码器，\n",
    "* 使用He初始化，\n",
    "* ELU激活函数\n",
    "* $ ℓ2 $正则化。\n",
    "\n",
    "代码应该看起来非常熟悉，除了没有标签（没有y）："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下载数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train all layers at once**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们构建一个堆叠的自动编码器，\n",
    "* 它有**3个隐藏层和1个输出层**（即2个堆叠的自动编码器）。 \n",
    "* 我们将使用ELU激活，He初始化和L2正则化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：由于`tf.layers.dense（）`函数与`tf.contrib.layers.arg_scope（）`（本书中使用的）不兼容，我们现在使用python的`functools.partial（）`函数。\n",
    "\n",
    "它可以很容易地创建一个`my_dense_layer（）`函数，该函数只调用自动设置所需参数的`tf.layers.dense（）`（除非在调用`my_dense_layer（）`时覆盖它们）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 300\n",
    "n_hidden2 = 150  # 编码\n",
    "n_hidden3 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01\n",
    "l2_reg = 0.0001\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "\n",
    "he_init = tf.contrib.layers.variance_scaling_initializer() # He 初始化\n",
    "\n",
    "# 等价于: he_init = lambda shape, dtype=tf.float32: tf.truncated_normal(shape, 0., stddev=np.sqrt(2/shape[0]))\n",
    "\n",
    "l2_regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
    "my_dense_layer = partial(tf.layers.dense,\n",
    "                         activation=tf.nn.elu,\n",
    "                         kernel_initializer=he_init,\n",
    "                         kernel_regularizer=l2_regularizer)\n",
    "\n",
    "hidden1 = my_dense_layer(X, n_hidden1)\n",
    "hidden2 = my_dense_layer(hidden1, n_hidden2)\n",
    "hidden3 = my_dense_layer(hidden2, n_hidden3)\n",
    "outputs = my_dense_layer(hidden3, n_outputs, activation=None)\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n",
    "\n",
    "reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
    "loss = tf.add_n([reconstruction_loss] + reg_losses)\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver() # not shown in the book"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在让我们训练吧！ 请注意，我们不提供目标值（不使用`y_batch`）。 这是无监督的训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09% Train MSE: 0.023920039\n",
      "19% Train MSE: 0.011482951\n",
      "29% Train MSE: 0.0102210175\n",
      "3 Train MSE: 0.009902515\n",
      "49% Train MSE: 0.010373088\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 5\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # not shown in the book\n",
    "            sys.stdout.flush()                                          # not shown\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch})\n",
    "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})   # not shown\n",
    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)           # not shown\n",
    "        saver.save(sess, \"./my_model_all_layers.ckpt\")                  # not shown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此函数加载模型，在测试集上对其进行评估（它测量重建误差），然后显示原始图像及其重建："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_reconstructed_digits(X, outputs, model_path = None, n_test_digits = 2):\n",
    "    with tf.Session() as sess:\n",
    "        if model_path:\n",
    "            saver.restore(sess, model_path)\n",
    "        X_test = mnist.test.images[:n_test_digits]\n",
    "        outputs_val = outputs.eval(feed_dict={X: X_test})\n",
    "\n",
    "    fig = plt.figure(figsize=(8, 3 * n_test_digits))\n",
    "    for digit_index in range(n_test_digits):\n",
    "        plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\n",
    "        plot_image(X_test[digit_index])\n",
    "        plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\n",
    "        plot_image(outputs_val[digit_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_all_layers.ckpt\n",
      "Saving figure reconstruction_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0495dc0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_reconstructed_digits(X, outputs, \"./my_model_all_layers.ckpt\")\n",
    "save_fig(\"reconstruction_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Tying weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当自动编码器整齐对称时，就像我们刚刚建立的那样，常用技术是将解码器层的权重与编码器层的权重联系起来。这样可以减少模型中的权重数量，加快训练速度并限制过拟合的风险。\n",
    "\n",
    "具体来说，如果自动编码器总共有 $N$ 层（不计入输入层），则 $W_L$表示第 $L$ 层的连接权重（例如，第1层是第一个隐藏层，第 $\\frac{N}{2}$层是编码层，第 $N$ 层是输出层），那么解码器层权重可以简单地定义为：$ W_{N-L+1} = W_L^T$\n",
    "$（L = 1,2，...，N2）$。\n",
    "\n",
    "不幸的是，使用`fully_connected（）`函数在TensorFlow中实现绑定权重有点麻烦;实际上，手动定义网络层实际上更容易。 结果是代码明显更加冗长："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通常将编码器和解码器的权重联系起来（`weights_decoder = tf.transpose（weights_encoder）`）。\n",
    "\n",
    "不幸的是，这使得使用`tf.layers.dense（）`函数变得不可能（或非常棘手），因此我们需要手动构建Autoencoder："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 300\n",
    "n_hidden2 = 150  # 编码\n",
    "n_hidden3 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01\n",
    "l2_reg = 0.0005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "activation = tf.nn.elu\n",
    "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "\n",
    "weights1_init = initializer([n_inputs, n_hidden1])\n",
    "weights2_init = initializer([n_hidden1, n_hidden2])\n",
    "\n",
    "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n",
    "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n",
    "weights3 = tf.transpose(weights2, name=\"weights3\")  # tied weights\n",
    "weights4 = tf.transpose(weights1, name=\"weights4\")  # tied weights\n",
    "\n",
    "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n",
    "biases2 = tf.Variable(tf.zeros(n_hidden2), name=\"biases2\")\n",
    "biases3 = tf.Variable(tf.zeros(n_hidden3), name=\"biases3\")\n",
    "biases4 = tf.Variable(tf.zeros(n_outputs), name=\"biases4\")\n",
    "\n",
    "hidden1 = activation(tf.matmul(X, weights1) + biases1)\n",
    "hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\n",
    "hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\n",
    "outputs = tf.matmul(hidden3, weights4) + biases4\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n",
    "reg_loss = regularizer(weights1) + regularizer(weights2)\n",
    "loss = reconstruction_loss + reg_loss\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这段代码非常简单，但有几点需要注意：\n",
    "\n",
    "* 第一，`weight3`和`weights4`不是变量，它们分别是权重2和权重1的转置（它们与它们“联系”）。\n",
    "\n",
    "* 其次，由于它们不是变量，因此将它们正规化是没有用的：我们只调整`weight1`和`weights2`。\n",
    "\n",
    "* 第三，偏见永远不会被束缚，也永远不会正规化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09% Train MSE: 0.01506695\n",
      "1 Train MSE: 0.016488748\n",
      "2 Train MSE: 0.017375939\n",
      "3 Train MSE: 0.016878333\n",
      "49% Train MSE: 0.015587721\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 5\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch})\n",
    "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
    "        saver.save(sess, \"./my_model_tying_weights.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_tying_weights.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04c320cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_reconstructed_digits(X, outputs, \"./my_model_tying_weights.ckpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Training One Autoencoder at a Time - 一次训练一个自动编码器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一次训练一个浅的自动编码器通常要快得多，而不是像我们刚才那样一次性训练整个堆叠自动编码器；然后将它们全部堆叠成一个堆叠的自动编码器（因此得名），如图15-4所示。 这对于非常深的自动编码器尤其有用。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 在训练的第一阶段期间，第一自动编码器学习重建输入。\n",
    "* 在第二阶段期间，第二自动编码器学习重建第一自动编码器的隐藏层的输出。\n",
    "\n",
    "* 最后，你只需使用所有这些自动编码器构建一个大三明治，如图15-4所示（即，你首先堆叠每个自动编码器的隐藏层，然后以相反的顺序堆叠输出层）。\n",
    "\n",
    "这为你提供了最终的堆叠自动编码器。你可以通过这种方式轻松训练更多自动编码器，构建一个非常深的堆叠自动编码器。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要实现这种多相训练算法，\n",
    "\n",
    "* **最简单的方法是为每个阶段使用不同的TensorFlow图**。训练自动编码器后，你只需通过它运行训练集并捕获隐藏层的输出。然后，该输出用作下一个自动编码器的训练集。一旦所有自动编码器都经过这种方式的训练，你只需复制每个自动编码器的权重和偏差，并使用它们构建堆叠自动编码器。实现这种方法非常简单，所以我们不会在这里详细说明，但请查看Jupyter笔记本中的代码作为示例。\n",
    "\n",
    "\n",
    "* **另一种方法是使用包含整个堆叠自动编码器的单个图形**，加上一些额外的操作来执行每个训练阶段，如图15-5所示。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这值得一点解释：\n",
    "\n",
    "* 图中的中心列是全堆叠自动编码器。这部分可以在训练后使用。\n",
    "\n",
    "* 左列是运行第一阶段培训所需的一组操作。它创建一个绕过隐藏层2和3的输出层。此输出层与堆叠自动编码器的输出层共享相同的权重和偏差。最重要的是训练操作，**旨在使输出尽可能接近输入**。因此，该阶段将训练隐藏层1和输出层（即第一自动编码器）的权重和偏差。\n",
    "\n",
    "* 图中的右列是运行第二阶段训练所需的一组操作。它增加了训练操作，**旨在使隐藏层3的输出尽可能接近隐藏层的输出**。请注意，我们必须在运行阶段2时冻结隐藏的第1层。该阶段将训练隐藏层2和3（即第二自动编码器）的权重和偏差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3.3.1  Training one Autoencoder at a time in multiple graphs - 在多个图形中一次训练一个Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有很多方法可以一次训练一个Autoencoder。\n",
    "\n",
    "* **第一种方法**是使用不同的图表训练每个Autoencoder\n",
    "\n",
    "* 然后我们通过简单地使用从这些自动编码器复制的权重和偏差来初始化它来创建Stacked Autoencoder。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们创建一个函数，该函数将训练一个自动编码器并返回变换后的训练集（即隐藏层的输出）和模型参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "def train_autoencoder(X_train, n_neurons, n_epochs, batch_size,\n",
    "                      learning_rate = 0.01, l2_reg = 0.0005, seed=42,\n",
    "                      hidden_activation=tf.nn.elu,\n",
    "                      output_activation=tf.nn.elu):\n",
    "    graph = tf.Graph()\n",
    "    with graph.as_default():\n",
    "        tf.set_random_seed(seed)\n",
    "\n",
    "        n_inputs = X_train.shape[1]\n",
    "\n",
    "        X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "        \n",
    "        my_dense_layer = partial(\n",
    "            tf.layers.dense,\n",
    "            kernel_initializer=tf.contrib.layers.variance_scaling_initializer(),\n",
    "            kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg))\n",
    "\n",
    "        hidden = my_dense_layer(X, n_neurons, activation=hidden_activation, name=\"hidden\")\n",
    "        outputs = my_dense_layer(hidden, n_inputs, activation=output_activation, name=\"outputs\")\n",
    "\n",
    "        reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n",
    "\n",
    "        reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
    "        loss = tf.add_n([reconstruction_loss] + reg_losses)\n",
    "\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "        training_op = optimizer.minimize(loss)\n",
    "\n",
    "        init = tf.global_variables_initializer()\n",
    "\n",
    "    with tf.Session(graph=graph) as sess:\n",
    "        init.run()\n",
    "        for epoch in range(n_epochs):\n",
    "            n_batches = len(X_train) // batch_size\n",
    "            for iteration in range(n_batches):\n",
    "                print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "                sys.stdout.flush()\n",
    "                indices = rnd.permutation(len(X_train))[:batch_size]\n",
    "                X_batch = X_train[indices]\n",
    "                sess.run(training_op, feed_dict={X: X_batch})\n",
    "            loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
    "            print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
    "        params = dict([(var.name, var.eval()) for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)])\n",
    "        hidden_val = hidden.eval(feed_dict={X: X_train})\n",
    "        return hidden_val, params[\"hidden/kernel:0\"], params[\"hidden/bias:0\"], params[\"outputs/kernel:0\"], params[\"outputs/bias:0\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在让我们训练两个Autoencoders。\n",
    "\n",
    "* 第一个是训练数据训练，\n",
    "* 第二个是训练前一个Autoencoder的隐藏层输出："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09% Train MSE: 0.01851794\n",
      "1 Train MSE: 0.01868272\n",
      "2 Train MSE: 0.018467719\n",
      "3 Train MSE: 0.019231759\n",
      "0 Train MSE: 0.0042360835\n",
      "19% Train MSE: 0.004832601\n",
      "2 Train MSE: 0.0046686814\n",
      "3 Train MSE: 0.0044038515\n"
     ]
    }
   ],
   "source": [
    "hidden_output, W1, b1, W4, b4 = train_autoencoder(mnist.train.images, n_neurons=300, n_epochs=4, batch_size=150,\n",
    "                                                  output_activation=None)\n",
    "_, W2, b2, W3, b3 = train_autoencoder(hidden_output, n_neurons=150, n_epochs=4, batch_size=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们可以**通过简单地重用我们刚刚训练过的Autoencoders的权重和偏差来创建Stacked Autoencoder**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28*28\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "hidden1 = tf.nn.elu(tf.matmul(X, W1) + b1)\n",
    "hidden2 = tf.nn.elu(tf.matmul(hidden1, W2) + b2)\n",
    "hidden3 = tf.nn.elu(tf.matmul(hidden2, W3) + b3)\n",
    "outputs = tf.matmul(hidden3, W4) + b4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04c69bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_reconstructed_digits(X, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.2  Training one Autoencoder at a time in a single graph - 在一个图表中一次训练一个Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**另一种方法**是使用单个图。为此，我们为完整的Stacked Autoencoder创建了图形，但是我们还添加了操作来独立训练每个Autoencoder：\n",
    "\n",
    "* **阶段1** 训练底层和顶层（即第一自动编码器），\n",
    "* **阶段2** 训练两个中间层（即第二自动编码器）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 300\n",
    "n_hidden2 = 150  # codings\n",
    "n_hidden3 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01\n",
    "l2_reg = 0.0001\n",
    "\n",
    "activation = tf.nn.elu\n",
    "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "\n",
    "weights1_init = initializer([n_inputs, n_hidden1])\n",
    "weights2_init = initializer([n_hidden1, n_hidden2])\n",
    "weights3_init = initializer([n_hidden2, n_hidden3])\n",
    "weights4_init = initializer([n_hidden3, n_outputs])\n",
    "\n",
    "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n",
    "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n",
    "weights3 = tf.Variable(weights3_init, dtype=tf.float32, name=\"weights3\")\n",
    "weights4 = tf.Variable(weights4_init, dtype=tf.float32, name=\"weights4\")\n",
    "\n",
    "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n",
    "biases2 = tf.Variable(tf.zeros(n_hidden2), name=\"biases2\")\n",
    "biases3 = tf.Variable(tf.zeros(n_hidden3), name=\"biases3\")\n",
    "biases4 = tf.Variable(tf.zeros(n_outputs), name=\"biases4\")\n",
    "\n",
    "hidden1 = activation(tf.matmul(X, weights1) + biases1)\n",
    "hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\n",
    "hidden3 = activation(tf.matmul(hidden2, weights3) + biases3)\n",
    "outputs = tf.matmul(hidden3, weights4) + biases4\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "\n",
    "with tf.name_scope(\"phase1\"):\n",
    "    phase1_outputs = tf.matmul(hidden1, weights4) + biases4  # bypass hidden2 and hidden3\n",
    "    phase1_reconstruction_loss = tf.reduce_mean(tf.square(phase1_outputs - X))\n",
    "    phase1_reg_loss = regularizer(weights1) + regularizer(weights4)\n",
    "    phase1_loss = phase1_reconstruction_loss + phase1_reg_loss\n",
    "    phase1_training_op = optimizer.minimize(phase1_loss)\n",
    "\n",
    "with tf.name_scope(\"phase2\"):\n",
    "    phase2_reconstruction_loss = tf.reduce_mean(tf.square(hidden3 - hidden1))\n",
    "    phase2_reg_loss = regularizer(weights2) + regularizer(weights3)\n",
    "    phase2_loss = phase2_reconstruction_loss + phase2_reg_loss\n",
    "    train_vars = [weights2, biases2, weights3, biases3]\n",
    "    phase2_training_op = optimizer.minimize(phase2_loss, var_list=train_vars) # freeze hidden1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 第一阶段相当简单：我们只创建一个跳过隐藏层2和3的输出层，然后构建训练操作以最小化输出和输入之间的距离（加上一些正则化）。\n",
    "\n",
    "\n",
    "* 第二阶段只是添加最小化隐藏层 3 的输出与隐藏层 1 之间的距离所需的操作（也具有一些正则化）。最重要的是，我们为`minim（）`方法提供了可训练变量列表，确保省去`weight1和biases1`;这有效地在阶段2期间冻结隐藏的第1层。\n",
    "\n",
    "* 在执行阶段，所有你需要做的就是为一些epochs运行第一阶段的训练操作，然后在第二阶段训练操作一些更多的epochs。\n",
    "\n",
    "\n",
    "由于隐藏层1在阶段2期间被冻结，因此对于任何给定的训练实例，其输出将始终相同。为了避免在每个时期重新计算隐藏层1的输出，你可以\n",
    "* 在阶段1结束时为整个训练集计算它，\n",
    "* 然后在阶段2期间直接提供隐藏层1的缓存输出。\n",
    "\n",
    "这可以为你带来不错的性能提升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training phase #1\n",
      "0 Train MSE: 0.007406866\n",
      "1 Train MSE: 0.0078288205\n",
      "2 Train MSE: 0.007728097\n",
      "3 Train MSE: 0.0074090064\n",
      "Training phase #2\n",
      "09% Train MSE: 0.35758996\n",
      "1 Train MSE: 0.00593613\n",
      "2 Train MSE: 0.0030089526\n",
      "3 Train MSE: 0.0024365915\n",
      "Test MSE: 0.009815397\n"
     ]
    }
   ],
   "source": [
    "training_ops = [phase1_training_op, phase2_training_op]\n",
    "reconstruction_losses = [phase1_reconstruction_loss, phase2_reconstruction_loss]\n",
    "n_epochs = [4, 4]\n",
    "batch_sizes = [150, 150]\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for phase in range(2):\n",
    "        print(\"Training phase #{}\".format(phase + 1))\n",
    "        for epoch in range(n_epochs[phase]):\n",
    "            n_batches = mnist.train.num_examples // batch_sizes[phase]\n",
    "            for iteration in range(n_batches):\n",
    "                print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "                sys.stdout.flush()\n",
    "                X_batch, y_batch = mnist.train.next_batch(batch_sizes[phase])\n",
    "                sess.run(training_ops[phase], feed_dict={X: X_batch})\n",
    "            loss_train = reconstruction_losses[phase].eval(feed_dict={X: X_batch})\n",
    "            print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
    "            saver.save(sess, \"./my_model_one_at_a_time.ckpt\")\n",
    "    loss_test = reconstruction_loss.eval(feed_dict={X: mnist.test.images})\n",
    "    print(\"Test MSE:\", loss_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3.3.3  Cache the frozen layer outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training phase #1\n",
      "09% Train MSE: 0.0075382586\n",
      "1 Train MSE: 0.007754701\n",
      "2 Train MSE: 0.007343679\n",
      "39% Train MSE: 0.007837775\n",
      "Training phase #2\n",
      "0 Train MSE: 0.18597357\n",
      "1 Train MSE: 0.0044647465\n",
      "2 Train MSE: 0.002462252\n",
      "3 Train MSE: 0.0020415403\n",
      "Test MSE: 0.009790834\n"
     ]
    }
   ],
   "source": [
    "training_ops = [phase1_training_op, phase2_training_op]\n",
    "reconstruction_losses = [phase1_reconstruction_loss, phase2_reconstruction_loss]\n",
    "n_epochs = [4, 4]\n",
    "batch_sizes = [150, 150]\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for phase in range(2):\n",
    "        print(\"Training phase #{}\".format(phase + 1))\n",
    "        if phase == 1:\n",
    "            hidden1_cache = hidden1.eval(feed_dict={X: mnist.train.images})\n",
    "        for epoch in range(n_epochs[phase]):\n",
    "            n_batches = mnist.train.num_examples // batch_sizes[phase]\n",
    "            for iteration in range(n_batches):\n",
    "                print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "                sys.stdout.flush()\n",
    "                if phase == 1:\n",
    "                    indices = rnd.permutation(mnist.train.num_examples)\n",
    "                    hidden1_batch = hidden1_cache[indices[:batch_sizes[phase]]]\n",
    "                    feed_dict = {hidden1: hidden1_batch}\n",
    "                    sess.run(training_ops[phase], feed_dict=feed_dict)\n",
    "                else:\n",
    "                    X_batch, y_batch = mnist.train.next_batch(batch_sizes[phase])\n",
    "                    feed_dict = {X: X_batch}\n",
    "                    sess.run(training_ops[phase], feed_dict=feed_dict)\n",
    "            loss_train = reconstruction_losses[phase].eval(feed_dict=feed_dict)\n",
    "            print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
    "            saver.save(sess, \"./my_model_cache_frozen.ckpt\")\n",
    "    loss_test = reconstruction_loss.eval(feed_dict={X: mnist.test.images})\n",
    "    print(\"Test MSE:\", loss_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4  Visualizing the Reconstructions - 可视化重建\n",
    "\n",
    "\n",
    "**确保自动编码器经过适当训练的一种方法是比较输入和输出**。它们必须非常相似，差异应该是不重要的细节。 让我们绘制两个随机数字及其重建："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_one_at_a_time.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c047ae4a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_test_digits = 2\n",
    "X_test = mnist.test.images[:n_test_digits]\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_model_one_at_a_time.ckpt\") # not shown in the book\n",
    "    outputs_val = outputs.eval(feed_dict={X: X_test})\n",
    "\n",
    "def plot_image(image, shape=[28, 28]):\n",
    "    plt.imshow(image.reshape(shape), cmap=\"Greys\", interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "for digit_index in range(n_test_digits):\n",
    "    plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\n",
    "    plot_image(X_test[digit_index])\n",
    "    plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\n",
    "    plot_image(outputs_val[digit_index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来足够接近。所以自动编码器已经正确地学会了重现它的输入，但是它学到了有用的特征吗？让我们来看看。\n",
    "\n",
    "\n",
    "## 3.5  Visualizing the extracted features - 可视化特征\n",
    "\n",
    "\n",
    "一旦你的自动编码器学学到了一些特征，你可能想看看他们。有各种各样的技术。可以说最简单的技术是考虑每个隐藏层中的每个神经元，并找到最能激活它的训练实例。\n",
    "\n",
    "\n",
    "* 这对于顶层隐藏层特别有用，因为它们通常捕获相对较大的特征，你可以轻松地在包含它们的一组训练实例中发现这些特征。例如，如果神经元在看到图片中的猫时强烈激活，那么激活它的图片最明显都包含猫。\n",
    "\n",
    "* 然而，对于较低层，这种技术不能很好地工作，因为这些特征更小，更抽象，因此通常很难准确理解神经元所兴奋的东西。\n",
    "\n",
    "\n",
    "让我们看看另一种技术。对于第一个隐藏层中的每个神经元，你可以创建一个图像，其中像素的强度对应于与给定神经元的连接的权重。例如，以下代码绘制了第一个隐藏层中五个神经元所学习的特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_one_at_a_time.ckpt\n",
      "Saving figure extracted_features_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04ca44ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_model_one_at_a_time.ckpt\") # not shown in the book\n",
    "    weights1_val = weights1.eval()\n",
    "\n",
    "for i in range(5):\n",
    "    plt.subplot(1, 5, i + 1)\n",
    "    plot_image(weights1_val.T[i])\n",
    "\n",
    "save_fig(\"extracted_features_plot\") # not shown\n",
    "plt.show()                          # not shown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前四个特征似乎对应于小补丁，而第五个特征似乎寻找垂直笔画（请注意，这些功能来自堆叠的去噪自动编码器，我们将在后面讨论）。\n",
    "\n",
    "\n",
    "另一种技术是为自动编码器提供随机输入图像，测量激活你感兴趣的神经元，然后执行反向传播以调整图像，使神经元更加激活。如果你多次迭代（执行渐变上升），图像将逐渐变成最令人兴奋的图像（对于神经元）。这是一种有用的技术，可视化神经元正在寻找的输入类型。\n",
    "\n",
    "最后，如果你使用自动编码器执行无监督预训练——例如，对于分类任务——**验证自动编码器学习的特征有用的一种简单方法是测量分类器的性能**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.  Unsupervised pretraining - 使用堆叠自动编码器进行无监督预训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "正如我们在第11章中讨论过的，\n",
    "\n",
    "* 如果你正在处理复杂的监督任务，但是你没有大量标记的训练数据，那么一种解决方案是找到执行类似任务的神经网络，然后重用其较低层。这使得仅使用很少的训练数据训练高性能模型成为可能，因为你的神经网络不必学习所有低级特征;它只会重用现有网络学到的特征探测器。\n",
    "\n",
    "\n",
    "* 同样，如果你有一个大型数据集，但大部分都是未标记的，你可以首先使用所有数据训练堆叠自动编码器，然后重复使用较低层为你的实际任务创建神经网络，并使用标记数据对其进行训练。例如，图15-8显示了如何使用堆叠自动编码器执行无监督预训练分类神经网络。\n",
    "\n",
    "如前所述，堆叠自动编码器本身通常一次训练一个自动编码器。在训练分类器时，如果你确实没有太多标记的训练数据，则可能需要冻结预训练的层（至少是较低的层）。"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这种情况实际上很常见，因为构建一个大的未标记数据集通常很便宜（例如，一个简单的脚本可以从互联网上下载数百万个图像），但标记它们只能由人类可靠地完成（例如，将图像分类为可爱或不）。标记实例是耗时且昂贵的，因此仅具有几千个标记实例是很常见的。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如我们之前讨论的，当前深度学习海啸的一个触发因素是Geoffrey Hinton等人在2006年的发现。深度神经网络可以以无监督的方式预先训练。他们使用受限制的玻尔兹曼机器（见附录E），但在2007年Yoshua Bengio等人表明自动编码器工作得很好。\n",
    "\n",
    "\n",
    "TensorFlow实现没有什么特别之处：只需使用所有训练数据训练自动编码器，然后重复使用其编码器层来创建新的神经网络（有关如何重用预训练层的更多详细信息，请参阅第11章，或查看Jupyter笔记本中的代码示例）。\n",
    "\n",
    "\n",
    "到目前为止，为了使自动编码器学习有趣的特征，我们限制了编码层的大小，使其不完整。实际上还有许多其他类型的约束可以使用，包括允许编码层与输入一样大，或者甚至更大，从而导致过度完整的自动编码器。我们现在来看看其中的一些方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们为MNIST分类创建一个小型神经网络：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 300\n",
    "n_hidden2 = 150\n",
    "n_outputs = 10\n",
    "\n",
    "learning_rate = 0.01\n",
    "l2_reg = 0.0005\n",
    "\n",
    "activation = tf.nn.elu\n",
    "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "y = tf.placeholder(tf.int32, shape=[None])\n",
    "\n",
    "weights1_init = initializer([n_inputs, n_hidden1])\n",
    "weights2_init = initializer([n_hidden1, n_hidden2])\n",
    "weights3_init = initializer([n_hidden2, n_outputs])\n",
    "\n",
    "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n",
    "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n",
    "weights3 = tf.Variable(weights3_init, dtype=tf.float32, name=\"weights3\")\n",
    "\n",
    "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n",
    "biases2 = tf.Variable(tf.zeros(n_hidden2), name=\"biases2\")\n",
    "biases3 = tf.Variable(tf.zeros(n_outputs), name=\"biases3\")\n",
    "\n",
    "hidden1 = activation(tf.matmul(X, weights1) + biases1)\n",
    "hidden2 = activation(tf.matmul(hidden1, weights2) + biases2)\n",
    "logits = tf.matmul(hidden2, weights3) + biases3\n",
    "\n",
    "cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "reg_loss = regularizer(weights1) + regularizer(weights2) + regularizer(weights3)\n",
    "loss = cross_entropy + reg_loss\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "correct = tf.nn.in_top_k(logits, y, 1)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "pretrain_saver = tf.train.Saver([weights1, weights2, biases1, biases2])\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "普通训练（无预训练）：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Train accuracy: 0.97333336 Test accuracy: 0.9334\n",
      "1 Train accuracy: 0.98 Test accuracy: 0.936\n",
      "2 Train accuracy: 0.97333336 Test accuracy: 0.9382\n",
      "39% Train accuracy: 0.9866667 Test accuracy: 0.9489\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 4\n",
    "batch_size = 150\n",
    "n_labeled_instances = 20000\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = n_labeled_instances // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "            indices = rnd.permutation(n_labeled_instances)[:batch_size]\n",
    "            X_batch, y_batch = mnist.train.images[indices], mnist.train.labels[indices]\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        accuracy_val = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "        print(\"\\r{}\".format(epoch), \"Train accuracy:\", accuracy_val, end=\" \")\n",
    "        saver.save(sess, \"./my_model_supervised.ckpt\")\n",
    "        accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})\n",
    "        print(\"Test accuracy:\", accuracy_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在重用我们预先训练的自动编码器的前两层："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_cache_frozen.ckpt\n",
      "09% Train accuracy: 0.9533333\tTest accuracy: 0.9294\n",
      "1 Train accuracy: 0.96666664\tTest accuracy: 0.9342\n",
      "2 Train accuracy: 0.98\tTest accuracy: 0.9449\n",
      "3 Train accuracy: 0.9866667\tTest accuracy: 0.9462\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 4\n",
    "batch_size = 150\n",
    "n_labeled_instances = 20000\n",
    "\n",
    "#training_op = optimizer.minimize(loss, var_list=[weights3, biases3])  # Freeze layers 1 and 2 (optional)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    pretrain_saver.restore(sess, \"./my_model_cache_frozen.ckpt\")\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = n_labeled_instances // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "            indices = rnd.permutation(n_labeled_instances)[:batch_size]\n",
    "            X_batch, y_batch = mnist.train.images[indices], mnist.train.labels[indices]\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        accuracy_val = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "        print(\"\\r{}\".format(epoch), \"Train accuracy:\", accuracy_val, end=\"\\t\")\n",
    "        saver.save(sess, \"./my_model_supervised_pretrained.ckpt\")\n",
    "        accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})\n",
    "        print(\"Test accuracy:\", accuracy_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.  Stacked denoising Autoencoder - 堆叠去噪自动编码器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**强制自动编码器学习有用特征的另一种方法是向其添加噪声输入**，训练它以恢复原始的无噪声输入。这可以防止自动编码器将其输入简单地复制到其输出，因此最终必须在数据中找到模式。\n",
    "\n",
    "自20世纪80年代以来，使用自动编码器去除噪声的想法一直存在（例如，在Yann LeCun 1987年的硕士论文中提到过）。\n",
    "\n",
    "* 在2008年的一篇论文中，Pascal Vincent等人表明自动编码器也可用于特征提取。\n",
    "\n",
    "* 在2010年的一篇论文中，Vincent等人介绍了**堆叠去噪自动编码器**。\n",
    "  * 噪声可以是添加到输入端的纯高斯噪声，\n",
    "  * 或者它可以随机关闭输入，就像在丢失中一样（在第11章中介绍）。\n",
    "  \n",
    "  图15-9显示了这两个选项。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 TensorFlow Implementation  - TensorFlow 实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在TensorFlow中实现去噪自动编码器并不太难。让我们从高斯噪声开始。 它实际上就像训练常规自动编码器一样，除了你为输入添加噪声，并且重建损失是根据原始输入计算的："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：本书使用`tf.contrib.layers.dropout（）`而不是`tf.layers.dropout（）`（本章编写时不存在）。现在最好使用`tf.layers.dropout（）`，因为contrib模块中的任何内容都可能更改或删除，恕不另行通知。`tf.layers.dropout（）`函数几乎与`tf.contrib.layers.dropout（）`函数相同，除了一些细微差别。\n",
    "\n",
    "最重要的是：\n",
    "  \n",
    "* 你必须指定the dropout rate (`rate`)而不是保持概率-the keep probability (`keep_prob`),其中 `rate`等于`1 - keep_prob`,\n",
    "* `is_training` 参数重命名为 `training`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用高斯噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 300\n",
    "n_hidden2 = 150  # codings\n",
    "n_hidden3 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_level = 1.0\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "X_noisy = X + noise_level * tf.random_normal(tf.shape(X))\n",
    "\n",
    "hidden1 = tf.layers.dense(X_noisy, n_hidden1, activation=tf.nn.relu,\n",
    "                          name=\"hidden1\")\n",
    "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book\n",
    "                          name=\"hidden2\")                            # not shown\n",
    "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown\n",
    "                          name=\"hidden3\")                            # not shown\n",
    "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\")        # not shown\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于`X`的形状仅在**构建阶段**被部分定义，我们事先无法知道必须添加到`X`的噪音的形状。\n",
    "\n",
    "* 我们不能调用`X.get_shape（`），因为这只会返回部分定义的`X`形状（[None，n_inputs]），而`random_normal（）`需要完全定义的形状，因此它会引发异常。 \n",
    "\n",
    "* 相反，我们调用`tf.shape（X）`，它创建一个在运行时将返回`X`形状的操作，该操作将在该点完全定义。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(reconstruction_loss)\n",
    "    \n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Train MSE: 0.044004317\n",
      "1 Train MSE: 0.042995136\n",
      "2 Train MSE: 0.041826446\n",
      "3 Train MSE: 0.04103372\n",
      "4 Train MSE: 0.040177725\n",
      "5 Train MSE: 0.038485397\n",
      "6 Train MSE: 0.039761316\n",
      "7 Train MSE: 0.04209128\n",
      "8 Train MSE: 0.039764088\n",
      "9 Train MSE: 0.040295053\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch})\n",
    "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
    "        saver.save(sess, \"./my_model_stacked_denoising_gaussian.ckpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 300\n",
    "n_hidden2 = 150  # codings\n",
    "n_hidden3 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropout_rate = 0.3\n",
    "\n",
    "training = tf.placeholder_with_default(False, shape=(), name='training')\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "X_drop = tf.layers.dropout(X, dropout_rate, training=training)\n",
    "\n",
    "hidden1 = tf.layers.dense(X_drop, n_hidden1, activation=tf.nn.relu,\n",
    "                          name=\"hidden1\")\n",
    "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book\n",
    "                          name=\"hidden2\")                            # not shown\n",
    "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown\n",
    "                          name=\"hidden3\")                            # not shown\n",
    "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\")        # not shown\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(reconstruction_loss)\n",
    "    \n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在**训练期间**我们必须使用`feed_dict`将`training`设置为`True`（如第11章所述）：\n",
    "```python\n",
    "sess.run(training_op, feed_dict={X: X_batch, is_training: True})\n",
    "```\n",
    "\n",
    "然而，在**测试期间**，没有必要将`training`设置为`False`，因为我们在调用`placeholder_with_default（）`函数时将其设置为默认值。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09% Train MSE: 0.02959963\n",
      "1 Train MSE: 0.029670006\n",
      "29% Train MSE: 0.026523616\n",
      "3 Train MSE: 0.026781008\n",
      "4 Train MSE: 0.026440252\n",
      "59% Train MSE: 0.026834942\n",
      "6 Train MSE: 0.026351236\n",
      "7 Train MSE: 0.027300986\n",
      "8 Train MSE: 0.026488299\n",
      "9 Train MSE: 0.02635668\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch, training: True})\n",
    "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
    "        saver.save(sess, \"./my_model_stacked_denoising_dropout.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_stacked_denoising_dropout.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c047ae44a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_reconstructed_digits(X, outputs, \"./my_model_stacked_denoising_dropout.ckpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.  Sparse Autoencoder - 稀疏自动编码器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**另一种通常会导致良好特征提取的约束是稀疏性**：通过向成本函数添加适当的项，推动自动编码器以减少编码层中的活动神经元的数量。例如，可以推动它在编码层中平均仅具有5％的显着活跃的神经元。这迫使自动编码器将每个输入表示为少量激活的组合。因此，编码层中的每个神经元通常最终代表一个有用的特征（如果你每个月只说几个字，你可能会试着让它们值得一听）。\n",
    "\n",
    "\n",
    "为了支持稀疏模型，我们必须\n",
    "\n",
    "* 首先在每次训练迭代时测量编码层的实际稀疏度。我们通过计算整个训练批次中编码层中每个神经元的平均激活来实现。批量大小不能太小，否则平均值不准确。\n",
    "\n",
    "* 一旦我们对每个神经元进行平均激活，我们想**通过在成本函数中添加稀疏性损失来惩罚过于活跃的神经元**。例如，如果我们测量神经元的平均激活为0.3，但目标稀疏度为0.1，则必须惩罚它以激活更少。\n",
    "\n",
    "   * 一种方法可以简单地将平方误差 $(0.3-0.1)^2$ 加到成本函数中，但实际上更好的方法是使用`Kullback-Leibler`散度（在第4章中简要讨论），它具有比均方误差更强的梯度，如图15-10所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sparsity_loss_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04a843470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 0.1\n",
    "q = np.linspace(0.001, 0.999, 500)\n",
    "kl_div = p * np.log(p / q) + (1 - p) * np.log((1 - p) / (1 - q))\n",
    "mse = (p - q)**2\n",
    "plt.plot([p, p], [0, 0.3], \"k:\")\n",
    "plt.text(0.05, 0.32, \"Target\\nsparsity\", fontsize=14)\n",
    "plt.plot(q, kl_div, \"b-\", label=\"KL divergence\")\n",
    "plt.plot(q, mse, \"r--\", label=\"MSE\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"Actual sparsity\")\n",
    "plt.ylabel(\"Cost\", rotation=0)\n",
    "plt.axis([0, 1, 0, 0.95])\n",
    "\n",
    "plt.title('Figure 15-10. Sparsity loss')\n",
    "save_fig(\"sparsity_loss_plot\")"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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PLFmyRJUoUcJYNZ988kljIcQnDasjliR81LAS4vvG9qV+/fqphg0bGg9+nsUyxQ4J3nX99debePHs/+abb4xv27hx48z72A2Br5cQlq3rrrvOeOIThjUNq6Tt/7Zo0SJTZqxf4XZeYG3G0leqVCnJ3vxu27bNWLPtQKyAWNb8ae009vX8+fNVhgwZDCZ2OP5/lCscibU1f/78XrKBAwea60ceeUQJVgRg1cSKSpmxqD/xxBOmzvg6Yq3D+gwG7Fr45JNPTB5Yk7FkY8nHykcbZ86c2XvX8OHDFe9u1aqVCaOdsOxnypRJDRo0yGCLZZW2++CDD0wasKRuQlhBeTdtAWY826RJE/Medk9gMd20aZMkT/YXyzh+ldmyZfPSYvFs3bq18f+kfJBYUfEZfP3119XatWtN+Ndff22sw/jL8hzWZHwqxReRstLfxRPCj1ty+S9evNjUkzYPR4wTsIDA7pprrjH9hDb86KOPTDhjIEeOHMZijHeGEG1Iv6KtxAJPHGUDe/FX5Rl8YyGssYyfYP6I9Gn8KuvWras+++wz47sr+fEbDks8EKSu+DvyfuoChlCo/kBZSS/tAu/BG8K2hpsMovkXDTdFImApyizIjMTsjFL9zTffNLO7KJXHjh1r0rBzQXzY0KXIkpB3IgEys2OVxbKGYYF8Eb9ZJkMYFrAio69iGYJukqUL74OY7XgG5TaWIyGkUZTionRlySl6GyQUdAoHDx6U5MY3jZnY1gUxuzFjUZdQxIzKrgLKgKoAS5sQS1GWSEhhLDGxqtv6MUkX7JelINhi4CEPcMOgIcTMbetSJFx+kZDBnnKxDLKXeCIVsQQRfS3SBIYQpFqkG6RhnkUyRKpBAc89emCxet96662mDEgxEMtgJD7eh8SBfs1Wl8jSHAmC/FmakSftJBIqkg71BXv6hRi3br/99gCLNv2AdOBg68Sk/v5fVhAYAVhO8hxSG/2AfoFlVTwZkP7BDaMWfYSysgwTQseK+obyQ+jYMFBgQEH1Q/llqUm8H7fk8qcP2ZZ7ea//F2x5L2oAkdIxZNCOompiicv4wkhnE0vcYKsXPDtoD9qVMY71WCRdlr7E2WNG8sSYyKrMxom45OqKHtRe/clYBmOR2EP1B5bk1Bd1FH0FbxB4yPlQOh6OlHEyM4fjuszazDhIG6NHjzazJ5Ij3JudFfj71axZ07wOnyFm87vvvtv4dTET4RPIvcwS+Ggx67IzAkIKZPYtXry4uWf3CLNQ/fr1A2Z5dnLY+ZCYsouvX5kyZczz8o/ZjvfbkhqzP7tk2I0QipCkbN8l/NOqVq3qJae8+EWyH7dWrVpm1vUiw1wgYclsJ8mQYEW6W7FihfFTs98l6fhFGrCb9eabbza+fZIGPzHqS7nwA0R6pN2Q2iEkbd7P3mK5Z7auXbu2ueff7NmzFTgisQvh/8nsTF3z5MkjweaXPoF/HFK3xJGeHUoXX3yxSUPfQopnv3KNGjW8cNLhp4c0ByEp4ONGGyPtJ0fUB5+1UAQGYAHhc0c56be805Zy6RP0Efudy5YtM5IM0g17jm3ivX7cwuWPb2XlypW9etp52dfspAKTe++918Ofe9rn0ksvNUlZ4SA1FilSxH5U0fa0G+PRT0iKO3bsMH2Y/iaEdIj0bUuQxLFKwM9y6dKlRspkdxcrBPoSFK6ulPf222/3dljRb5DYqRPjCArVH4ijf7ISoy7sQZfdTubBGP5FxQhjyN88wlKWBgYYuxPFmp97ziHgEPhnEUAIYJMES3HUWTAlGCFqBwSARKOodYSxVBDQkAIcE4wFPfeMQ+DCQoDVBJI8EjNMEGJ1MmnSJMUqDukx0SjFGSHLGJbJF110kdmCl2gAufI6BBwCgQgwllFPYRBDRcFSnaUqRk4OtRAVRuBTF/Zdii6NYYLsHWTPIxZIJEKsko4cAg6BxEaAMc1+ffTR6H/RQ7ds2dJ4YyRizVKUESYiIK7MDgGHQNpDIMWXxmkPUldjh4BDINEQcIww0VrMldch4BCIOwKOEcYdUpehQ8AhkGgIOEaYaC3myusQcAjEHQHHCOMOqcvQIeAQSDQEHCNMtBZz5XUIOATijoBjhHGH1GXoEHAIJBoCjhEmWou58joEHAJxR8AxwrhD6jJ0CDgEEg0BxwgTrcVceR0CDoG4I+AYYdwhdRk6BBwCiYaAY4SJ1mKuvA4Bh0DcEXCMMO6QugwTFQH7VG9/HTiDz1HqRcAxwtTbtmm+Znwcqnnz5uajYRwz36lTJ8WHqzhenjP0bOIwUT5aZTNDmB/HyO3atct8yIjPEzhKpQiczwdP3LMOgURAgA8P9e3b1xSVD3TxsR8+EsYHpCDC7rjjDr1z586A6vARMT5MtGrVKvMxIr6zzSdWHaU+BJxEmEonOFet/yLAp1ohPhQE8aEoPh/LR5fkM5f/+c9/1C233GI+RWsS/e8fn5dAUrzpppvMCet9+/ZVDz/8cMCnUO307jpxEXCMMHHbzpU8AgSWL19uUnGCshBfTIP4yhvfzv7www8Nc5R4+YUJ8mU1Ib6Yxjd1+dqco9SFgGOEqas9XW18CPCBcT5NKZ+5JJrPdfLpTT4mPm/ePJU3b94k0iCfhOXTnW+//XZAjvXq1VMTJ04MCHM3iY+AY4SJ34auBmEQ4Ju7MLq9e/eqDRs2qMcff1zxHWu+qwstXrxYlS1bNkkOfJsXZsmS2Sa+E8yy2lHqQiBD6qpO2qgNls3jx49HVNmsWbOe98evI3rRBZqIpTHf1Ea/x9fX0BV+8MEHKkOG/3b9nTt3eh+ct6vAMzDJf//733awSQtTdZS6EEgoRojLQzy+jbx+/Xp1ww03mJZct26d923WeDQt+ie+2JeSBA4o8s+cOaMKFSqkcufO7b2OsMOHD6vt27ebsO+//15VrFjRi09LF8eOHVObN282OsA777wzaNX5ApswRTsBS2ri/NiRlnBHqQuBhFkaM6BfeOGF80b/9OnTql+/fl4+/hnfi4jxok+fPgEK9hizCftY9uzZ1YsvvmjSsIRbtGiRAh/+fvrpJ7Vt2zZznSVLFnXgwIGwef3dkefOnfvbXgkzg4ItfaUQGD8wmPhpwYIFqlKlSipjxowBUfgm8oyj1IVATIyQzsBsK3+nTp1KUVSQsnB5eOmllwLeI+/nVyyBAQlS6IblFEp2BhoSmE0w686dOxu3Czs83tfdunVTFSpUUCtXrgw6QSDJoA8LxgiRaGzsgjECu7xbtmyxbwOuN27cGHAf7Gb//v3qX//6l4KBw2D+LpoyZYpiogj3wXFcY5Aa/bRw4UJVuHBh9c477wREIWlfe+21AWHuJvERiIkRYkljSZYjRw7Vo0cP1atXL+PBf9ttt6lJkybFHZXXXntNNW3aNMDyx+DlvXR0XCPeeust9dBDD6nq1asrcZmId0HYYVCnTh31+uuvq5MnTxqpix0L6JyEkMIeffRRBaNKSULfNXz4cOMX9/LLLwet81NPPZVkaUeZUA2AFdjVrVtXge8dd9xhMPYzTiTmJUuWBFQFfJGWmAR4t+1iEpDwfze5cuUyzNq/myNY2niE0TaPPfaY+vjjjxV+hA888IBCBRKMatSoYXSB/rgdO3ao3bt3G5zsOLCoXbu2HeSuUwMCsfqIV6xYUd93330Bj//44486e/bsumvXrgHh53OD13/OnDn1rl27gmbDroHBgwd7cR06dNC5cuXSp06d8sLsC8JbtGjhBTVu3Ni7DnexcOFCU47Ro0cHJDt8+LDOmzevHjhwoBd+5MgRfdlll2l+U5peffVVDQbFixc3OyQifd8333xjntuwYYN5hF0WZcqU0fXr1/eymDBhgm7VqpV3LxeLFi3SrVu3NrdnzpzR5cqV05s2bZLooL+///67ed/cuXODxv+TgaVKldLz588PKEKwtjt79qzpWxs3bgxI624SHwH2VsZE+fLl06+99lqSZ2FKDMxly5YliYslYNKkSbps2bJBH92xY4d5F1ughKZOnWrCVqxYIUEBv7Ewwj/++EPnz58/JIPv3bu3vvLKKwMYUfXq1bWfaQYUJE43f/75p2ZSAvMuXbpEnOuIESP0FVdcEZC+c+fOOmvWrCYMnAoWLKjZZpYcjRo1StepUydssguZES5YsECzfS45GjJkiG7btm1yyVx8AiIQ09IYj3uWiaVLl04iFLPEgnA9iAehqylfvnzQrFiiocwuXry4F4+hACpYsKAXdr4XuF7s27dPdenSJWhW7Dhg2ceSUwj9XXJLRkl7Pr8skT/99FOzRO7fv3/EuONfh8OwTWCHygMaOXKksaxjnbaJgwg++ugjhcOxUIMGDdQ333yjItEXyjPySz7oVFEnjBo1SoK9X3Sxzz77rFE1DBs2TA0ePDiJk7OXOMYL1CnVqlVTI0aMCJnDpk2b1GeffabeeOONkGlcROIiEJP7DJZJCAbgJxk4so/THx/t/apVq1T9+vWDPsZgLlGihOf+gEWYQcrAjIebjbz0iy++UDVr1lRXX321BAX8irsMjATlO1SyZEk1aNCggHT2zdq1a9X8+fPtoIBrJpkqVaoEhIW6KVasmPGTe+aZZ8zpKpFgzyRiTzAwsVmzZhmdH++ZMWNGEl0YBhaYEgwDnawQelF0hpzOcv3110twsr9MLEyqMHL8ItG98V6YMARjxEC2YsUKo5NlhwhlxjIfb0LXHW7HCE7Y7Eihro5SHwIxSYRYS2F4waxxzJxQvnz54oIWzIWdAcGIwYwFdOzYsUZSYDBiOJGBFOyZaMMwCMBYihYtGvJR8dkTaYqEGAiQmkMRkhw+aaH+iI+GYCq8E8NAcoRDNpMZ5Wbwv/rqq+quu+5SvXv3Vu3btzePr1mzxvO1lPwyZ85sGC7GCIxENjEhhTJI2OnkmhUDEqy4MuH4jdEG5ifHXQ0YMMA4QHNQQrZs2VStWrWM8QNpOyWoYcOGIbPF6m1v0wuZ0EUkJAIxSYQwoGDLYhAQ3614WdZwzQk1C1MOllUMlJw5c6pp06aFZJqxtg5WVJhtgQIFQmaBZAqTCMcs/Q/j0C1O3f64WO4HDhxolrXiXxguD5bwJ06cMOfvIZGVK1fOSF0i2fIsTNw/0XFIAe4vWJthfDbhWycTgh0e6hppmGfsCbNq1aomOXE4QPM+W92AlwKuW6EIKfufOBCBsoYaD6HK6sIvLARiYoRIE40bN05SExgG+jR0LvayK0nCKAPSpUuX5AkGKkwK9we2Q6UUMVBhxOgIg9HZs2fVuHHjjJ8jAzdSYimKI3QogtFEKvnAADhsFIfq9OnTh8rSC2cCgXGjz41W8kTvSfueL+GagmRJn5EyIx0jdUl7M8m1aNFCfffdd4YpM9EF0yOeb1nc8w6BqBkhjssM4mAzIKf54t8nnZVlFw7GdHZ+77//foXCu2fPnua6Xbt2iiUYUgw7DvALbNKkSUCrMGCRXvyEFAb5Ff7+dPG4v++++4z+7M0330ySHcs5JBt0Z9EQki7SWChKzslZnmPpDmaUAx1aJATDlDP2QqVnAmCrnp+QCPHp9BP1QWKLlGDyHH/FpCqTJtIe9UbFAd19992GWc+dO9dsW/z222/DSuYYzWzDWaRlcekcAlEzQjm1Q6DDQIEyG10PsznSBstUCJ0Lym+kJpgghL4NR2BZOqPoR0nPIPAzQdKjdwu2yR0pDL3R37HdCQdqTiFhl0GHDh1MPfgnukmYA/o5m2By9rLPjuMaYwp/50vdu3c3DLB169YRZYV+kAmK5XA4QiJF0uTYKZs4eQUVwPjx4wP0katXrzbtbae1r/fs2WNuRbLmCH0MLjhss5xFMh0zZoyRNsU4hsRKv2DCpJ1hzOiLRYK08w93jXM072Dpzm4RVhGh1BLoLvE4yJ8/v5clfRxpFQkeFYnbWeJBk2ouotLIv/vuu2ro0KGms7Kbgp0LSEJ42+PWMHr0aI8JCkIowWFYEFILOxmECUqayy67TPEXjJBcGMWLYp4AAAbGSURBVGRCsmuAgYU0yBI9GiW95BPNL4OCrWxIr0hDGGTYv8oAg1kEk8SwdosFOZp3RZN2zpw5hiENGTIk5GNgLft7KeuDDz5oyosFuE2bNopBHowwniCJ2YS0BuZMarZRhokOCd3frvIsOLF0Z0nNBAZDEcYCU6FMWG3Z6jZ9+nRvaYya5dChQ0YqZNcSqxD6UzR6QAxBTLAwfoxBtEmzZs08I42UkV8mNHStNhMkHAmVVQvvb9u2bVgjmJ2fu04gBFLa9xEn3Xbt2unnn39eL168OOjrSPP4448HjWN3A9+XiBfF4lDtf/ewYcPMDppixYrp2bNn+6PNPQ7Vw4cPDxoXj8CDBw+aHS3z5s0Lmd3YsWONs3XIBGEicCLPkyeP3rNnT5hU/40aN25csg7VyWYSJAG7UNq3b69PnDhhYvmmSO3atSNyfuYBdg3hjH/8+PGA3Pfv368vvvhiPWXKFC/80KFDumTJkvro0aNemFyMHDlSb9261dwuXbrUYHru3DmJdr+pAIGol8ax8HiWUxyKiTEhWsMGSzMOXGDjvn/5GUtZ4vFMy5YtjWTLsvTee+81uiz2X7PsgvCJw3pOXEpRq1atjOSEqsKvrkAny0EJM2fOVB07doypCCxFcW9BikLaD0VIg+hOWdbGk1At3HPPPabfiNcAqgbcWCLxk8TnDwdopHjbGk4ZUd2wZxwpT5b+rFbQBcvqxa4L+7KF0GeiC6W+fL/EUSpBIKWZOdIe+3+XLFmiM2fOHHTbWTiJkPI9++yzesCAAXEpajwkQrsgBw4c0HPmzNEzZszwgtl3/Mgjj3j38b5ASmZLXSR/bFE8H0KStyUnf14dO3ZMsk/XnyaW+23btun06dPrHj166N9++81kwX7zevXq6X379iWbZYECBczX6kIlZDsi/ZE91kh3bC38+eefkyRHqvdv8fzkk0/0rbfemiStC0hcBGLeaxxplYURkn78+PE6U6ZMmg3/NgVjhPZeYZY2FSpUCHmQgp1XJNd2h7f3KUfybHJpOISgdOnSeu/evckljTn+2LFjmo3/kfxRnvOlzZs3B80CJiKHNgRNcJ6BMHEOk8iSJYuuVauWfvrppzUTT3LEMpZJ4t133w2ZlINBSIMKgL6WLVu2JGlhePfff78uUqRIQNzatWt1xowZdTywDcjY3fxjCKQ4I+SklyZNmngV7NevnzmggJNqhEjTtGlTudXr16/X3bp18+65QKJEOrjQqU+fPikiIUVSbySlli1bRpI0VaeZPHmyYXK2lO6vcKNGjYyOlXAOx2DyCkZPPPGEfvTRRwOi0FnCRLds2RIQ7m4SF4EU1RGiu8LpmY36uMzgHIuVGUsgPoe4RXBeHGnQNXXt2tUc3omfm1+3hfsKH8650AmrIgeQ/hOEC0qk+5P/ifL9Xe+Uw3LZAROM0KFySASWaAgLeqjtc1iS8W+1SXSW4Xa52OnddQIgkLg83JXcjwBHcgWzevrTpfb71atXG4kNa3YwkqPaVq5caaIHDRqkK1eunCQp1mUkP457swmcCV+zZo0d7K4TGIGo/AgTgK+n2SLia8luDU7fSevEZzg5ViuYJZudUeL/Kh9958Qccfi2sUMaxKGf3U3ii0k8HgxQqNOI7DzcdWIg4BhhYrRTsqXERQS3kEj3JyebYYInwMmco7Nw7WEpjDsOBziw2wnXIzn1hmrCEDnlCDWNTeyYQmXDsV/syBEiH1x54nnUm+Ttfv8ZBNIhzf4zr3ZvjScC6GH5rCcflorm8Id4luFCywvpj7MTOdyVbYVIcEh5wU4JYtsdOmv7MBF2wTA8/Fs/0XOzy8b/YacLrf6uPFEgkMDLeld0C4Hly5frqlWrWiHu0kZg5syZulChQuabI0OHDrWjzDXfLLn99tuThPsD8EO95pprdCiXIn96d58YCLilcRSTxoWclP27HBbwd3we4ELGIVTZ2DvNXmsOzWC3DCoEls5CnEAOfsntY+bEbDwe3MELglzq+HWMMHW0o9GDMThZ4jkKjgBuLzAxDoHg8Ff/4QoYmjgtKdTJ4pycTR4cVuEodSHgdISpqz1dbVIQAfSw0R5km4LFcVnHEQHHCOMIpsvKIeAQSEwE3NI4MdvNldoh4BCIIwKOEcYRTJeVQ8AhkJgIOEaYmO3mSu0QcAjEEQHHCOMIpsvKIeAQSEwEHCNMzHZzpXYIOATiiIBjhHEE02XlEHAIJCYCjhEmZru5UjsEHAJxRMAxwjiC6bJyCDgEEhMBxwgTs91cqR0CDoE4IuAYYRzBdFk5BBwCiYmAY4SJ2W6u1A4Bh0AcEfh/UwMgDnI+IkoAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给定两个离散概率分布 $P$ 和 $Q$，$KL$之间的差异,注意到$D_{KL}（P∥Q）$，可以使用公式15-1计算这些分布。\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在我们的例子中，我们想要测量编码层中的神经元将激活的目标概率 $p$ 与实际概率 $q$ 之间的偏差（即，训练批次的平均激活）。因此，KL 差异简化为公式15-2。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一旦我们计算了编码层中每个神经元的稀疏度损失，我们只是累加这些损失，并将结果添加到成本函数中。为了控制稀疏性损失和重建损失的相对重要性，我们可以将稀疏性损失乘以稀疏度权重超参数。\n",
    "\n",
    "* 如果此权重太高，模型将紧贴目标稀疏度，但它可能无法正确重建输入，使模型无用。\n",
    "* 相反，如果它太低，模型将主要忽略稀疏性目标，它不会学习任何有趣的特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1  TensorFlow Implementation - TensorFlow 实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 1000  # sparse codings - 稀疏编码\n",
    "n_outputs = n_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kl_divergence(p, q):\n",
    "    # Kullback Leibler divergence\n",
    "    return p * tf.log(p / q) + (1 - p) * tf.log((1 - p) / (1 - q))\n",
    "\n",
    "learning_rate = 0.01\n",
    "sparsity_target = 0.1\n",
    "sparsity_weight = 0.2\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])            # not shown in the book\n",
    "\n",
    "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.sigmoid) # not shown\n",
    "outputs = tf.layers.dense(hidden1, n_outputs)                     # not shown\n",
    "\n",
    "hidden1_mean = tf.reduce_mean(hidden1, axis=0) # batch mean\n",
    "sparsity_loss = tf.reduce_sum(kl_divergence(sparsity_target, hidden1_mean))\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE\n",
    "loss = reconstruction_loss + sparsity_weight * sparsity_loss\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "08% Train MSE: 0.13483515 \tSparsity loss: 0.42173785 \tTotal loss: 0.21918273\n",
      "1 Train MSE: 0.058796614 \tSparsity loss: 0.010896608 \tTotal loss: 0.060975935\n",
      "2 Train MSE: 0.053752076 \tSparsity loss: 0.020105548 \tTotal loss: 0.057773184\n",
      "3 Train MSE: 0.047628514 \tSparsity loss: 0.039966185 \tTotal loss: 0.05562175\n",
      "4 Train MSE: 0.044760786 \tSparsity loss: 0.011620072 \tTotal loss: 0.0470848\n",
      "5 Train MSE: 0.04037644 \tSparsity loss: 0.09304121 \tTotal loss: 0.058984682\n",
      "68% Train MSE: 0.03884226 \tSparsity loss: 0.04629005 \tTotal loss: 0.04810027\n",
      "7 Train MSE: 0.03782742 \tSparsity loss: 0.07588796 \tTotal loss: 0.053005014\n",
      "8 Train MSE: 0.033216964 \tSparsity loss: 0.020068452 \tTotal loss: 0.037230656\n",
      "9 Train MSE: 0.031438425 \tSparsity loss: 0.0965075 \tTotal loss: 0.050739925\n",
      "10 Train MSE: 0.027383836 \tSparsity loss: 0.0670876 \tTotal loss: 0.040801354\n",
      "11% Train MSE: 0.024683421 \tSparsity loss: 0.09008203 \tTotal loss: 0.04269983\n",
      "12 Train MSE: 0.02333605 \tSparsity loss: 0.05774161 \tTotal loss: 0.03488437\n",
      "13% Train MSE: 0.022899937 \tSparsity loss: 0.062330432 \tTotal loss: 0.035366025\n",
      "14 Train MSE: 0.02109492 \tSparsity loss: 0.025819102 \tTotal loss: 0.02625874\n",
      "15% Train MSE: 0.02200285 \tSparsity loss: 0.4832065 \tTotal loss: 0.11864415\n",
      "16% Train MSE: 0.019056583 \tSparsity loss: 0.036143437 \tTotal loss: 0.02628527\n",
      "17% Train MSE: 0.018892387 \tSparsity loss: 0.132694 \tTotal loss: 0.04543119\n",
      "18% Train MSE: 0.017418984 \tSparsity loss: 0.040309094 \tTotal loss: 0.025480803\n",
      "19% Train MSE: 0.017863313 \tSparsity loss: 0.11048472 \tTotal loss: 0.039960258\n",
      "20% Train MSE: 0.016831411 \tSparsity loss: 0.029139595 \tTotal loss: 0.02265933\n",
      "21% Train MSE: 0.018388988 \tSparsity loss: 0.36421055 \tTotal loss: 0.0912311\n",
      "22 Train MSE: 0.016125064 \tSparsity loss: 0.05562851 \tTotal loss: 0.027250767\n",
      "23% Train MSE: 0.015894528 \tSparsity loss: 0.07925518 \tTotal loss: 0.031745564\n",
      "24% Train MSE: 0.015703432 \tSparsity loss: 0.1492547 \tTotal loss: 0.04555437\n",
      "25% Train MSE: 0.014533915 \tSparsity loss: 0.13618498 \tTotal loss: 0.041770913\n",
      "26 Train MSE: 0.014423798 \tSparsity loss: 0.110555105 \tTotal loss: 0.03653482\n",
      "27% Train MSE: 0.013853267 \tSparsity loss: 0.074466795 \tTotal loss: 0.028746627\n",
      "28% Train MSE: 0.01393316 \tSparsity loss: 0.15847737 \tTotal loss: 0.045628633\n",
      "29 Train MSE: 0.013378657 \tSparsity loss: 0.14383566 \tTotal loss: 0.04214579\n",
      "30% Train MSE: 0.01372851 \tSparsity loss: 0.18564183 \tTotal loss: 0.050856877\n",
      "31 Train MSE: 0.013954376 \tSparsity loss: 0.06351213 \tTotal loss: 0.026656803\n",
      "32% Train MSE: 0.013694251 \tSparsity loss: 0.057796445 \tTotal loss: 0.02525354\n",
      "33% Train MSE: 0.013471983 \tSparsity loss: 0.10417083 \tTotal loss: 0.03430615\n",
      "34 Train MSE: 0.012443058 \tSparsity loss: 0.13656648 \tTotal loss: 0.039756354\n",
      "35 Train MSE: 0.012658664 \tSparsity loss: 0.16290024 \tTotal loss: 0.04523871\n",
      "36 Train MSE: 0.0128786275 \tSparsity loss: 0.09486508 \tTotal loss: 0.031851646\n",
      "37% Train MSE: 0.012347986 \tSparsity loss: 0.10808721 \tTotal loss: 0.033965427\n",
      "38% Train MSE: 0.012169593 \tSparsity loss: 0.20088781 \tTotal loss: 0.052347157\n",
      "39 Train MSE: 0.012255683 \tSparsity loss: 0.14940624 \tTotal loss: 0.04213693\n",
      "40% Train MSE: 0.012600224 \tSparsity loss: 0.23064898 \tTotal loss: 0.05873002\n",
      "41 Train MSE: 0.012468431 \tSparsity loss: 0.10066365 \tTotal loss: 0.032601163\n",
      "42% Train MSE: 0.01169707 \tSparsity loss: 0.101108566 \tTotal loss: 0.031918783\n",
      "43 Train MSE: 0.012214752 \tSparsity loss: 0.12578952 \tTotal loss: 0.037372656\n",
      "44 Train MSE: 0.011719501 \tSparsity loss: 0.11030118 \tTotal loss: 0.033779737\n",
      "45 Train MSE: 0.011691931 \tSparsity loss: 0.16508272 \tTotal loss: 0.044708475\n",
      "46% Train MSE: 0.01161325 \tSparsity loss: 0.13042861 \tTotal loss: 0.037698973\n",
      "47 Train MSE: 0.0117383385 \tSparsity loss: 0.16350412 \tTotal loss: 0.044439163\n",
      "48% Train MSE: 0.011652929 \tSparsity loss: 0.4595864 \tTotal loss: 0.103570215\n",
      "49% Train MSE: 0.011667955 \tSparsity loss: 0.1881171 \tTotal loss: 0.049291376\n",
      "50% Train MSE: 0.011429663 \tSparsity loss: 0.19221127 \tTotal loss: 0.049871918\n",
      "51 Train MSE: 0.011397637 \tSparsity loss: 0.26645532 \tTotal loss: 0.0646887\n",
      "52 Train MSE: 0.011935532 \tSparsity loss: 0.37998408 \tTotal loss: 0.08793235\n",
      "53 Train MSE: 0.011303912 \tSparsity loss: 0.12977052 \tTotal loss: 0.037258014\n",
      "54 Train MSE: 0.015308278 \tSparsity loss: 0.43482608 \tTotal loss: 0.102273494\n",
      "55% Train MSE: 0.013402332 \tSparsity loss: 0.083303265 \tTotal loss: 0.030062985\n",
      "56 Train MSE: 0.012301463 \tSparsity loss: 0.29758972 \tTotal loss: 0.07181941\n",
      "57 Train MSE: 0.01220161 \tSparsity loss: 0.2470309 \tTotal loss: 0.061607793\n",
      "58% Train MSE: 0.023724657 \tSparsity loss: 0.2157228 \tTotal loss: 0.066869214\n",
      "59% Train MSE: 0.013819309 \tSparsity loss: 0.20356952 \tTotal loss: 0.054533213\n",
      "60% Train MSE: 0.018737163 \tSparsity loss: 0.18045998 \tTotal loss: 0.054829158\n",
      "61% Train MSE: 0.012983549 \tSparsity loss: 0.23172164 \tTotal loss: 0.059327878\n",
      "62% Train MSE: 0.014322434 \tSparsity loss: 0.4988729 \tTotal loss: 0.114097014\n",
      "63 Train MSE: 0.014641505 \tSparsity loss: 0.33430344 \tTotal loss: 0.0815022\n",
      "64 Train MSE: 0.011960664 \tSparsity loss: 0.17661525 \tTotal loss: 0.047283713\n",
      "65% Train MSE: 0.016084477 \tSparsity loss: 0.17382067 \tTotal loss: 0.050848614\n",
      "66% Train MSE: 0.01511671 \tSparsity loss: 1.0214244 \tTotal loss: 0.2194016\n",
      "67% Train MSE: 0.037081353 \tSparsity loss: 0.32537615 \tTotal loss: 0.10215659\n",
      "68 Train MSE: 0.014882477 \tSparsity loss: 0.23073672 \tTotal loss: 0.06102982\n",
      "69 Train MSE: 0.012663725 \tSparsity loss: 0.45444065 \tTotal loss: 0.10355186\n",
      "70% Train MSE: 0.042739876 \tSparsity loss: 0.7453109 \tTotal loss: 0.19180205\n",
      "71 Train MSE: 0.014040967 \tSparsity loss: 0.23101452 \tTotal loss: 0.060243875\n",
      "72% Train MSE: 0.016177107 \tSparsity loss: 0.8284675 \tTotal loss: 0.18187061\n",
      "73% Train MSE: 0.013968509 \tSparsity loss: 0.31662983 \tTotal loss: 0.07729447\n",
      "74% Train MSE: 0.03330112 \tSparsity loss: 0.28925762 \tTotal loss: 0.09115264\n",
      "75% Train MSE: 0.031958304 \tSparsity loss: 0.4885584 \tTotal loss: 0.12967\n",
      "76% Train MSE: 0.021692961 \tSparsity loss: 0.20174679 \tTotal loss: 0.062042322\n",
      "77 Train MSE: 0.02072525 \tSparsity loss: 0.3239027 \tTotal loss: 0.08550579\n",
      "78% Train MSE: 0.016116608 \tSparsity loss: 0.53371567 \tTotal loss: 0.122859746\n",
      "79% Train MSE: 0.020076249 \tSparsity loss: 0.42562547 \tTotal loss: 0.10520135\n",
      "80 Train MSE: 0.01660693 \tSparsity loss: 0.11470247 \tTotal loss: 0.039547425\n",
      "81 Train MSE: 0.012946971 \tSparsity loss: 0.9125188 \tTotal loss: 0.19545072\n",
      "82 Train MSE: 0.016042588 \tSparsity loss: 2.1753566 \tTotal loss: 0.4511139\n",
      "83% Train MSE: 0.015394733 \tSparsity loss: 0.68296057 \tTotal loss: 0.15198685\n",
      "84 Train MSE: 0.014096205 \tSparsity loss: 0.29207104 \tTotal loss: 0.072510414\n",
      "85% Train MSE: 0.025468064 \tSparsity loss: 0.9509176 \tTotal loss: 0.21565159\n",
      "86 Train MSE: 0.014383582 \tSparsity loss: 0.6854093 \tTotal loss: 0.15146545\n",
      "87 Train MSE: 0.012321889 \tSparsity loss: 1.4474982 \tTotal loss: 0.30182153\n",
      "88 Train MSE: 0.018260745 \tSparsity loss: 0.8596282 \tTotal loss: 0.1901864\n",
      "89 Train MSE: 0.034750056 \tSparsity loss: 0.29357794 \tTotal loss: 0.09346564\n",
      "90 Train MSE: 0.025785366 \tSparsity loss: 0.44550833 \tTotal loss: 0.11488703\n",
      "91 Train MSE: 0.014288821 \tSparsity loss: 0.08939013 \tTotal loss: 0.032166846\n",
      "92 Train MSE: 0.016562771 \tSparsity loss: 0.39101246 \tTotal loss: 0.09476526\n",
      "93% Train MSE: 0.016320081 \tSparsity loss: 0.15611926 \tTotal loss: 0.047543935\n",
      "94 Train MSE: 0.012143337 \tSparsity loss: 0.12010062 \tTotal loss: 0.03616346\n",
      "95% Train MSE: 0.01396099 \tSparsity loss: 0.10600659 \tTotal loss: 0.035162307\n",
      "96 Train MSE: 0.01786058 \tSparsity loss: 0.23058423 \tTotal loss: 0.06397743\n",
      "97% Train MSE: 0.013293912 \tSparsity loss: 0.102366954 \tTotal loss: 0.0337673\n",
      "98% Train MSE: 0.013645646 \tSparsity loss: 0.083595574 \tTotal loss: 0.030364763\n",
      "99% Train MSE: 0.025408482 \tSparsity loss: 0.33418158 \tTotal loss: 0.092244804\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 100\n",
    "batch_size = 1000\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch})\n",
    "        reconstruction_loss_val, sparsity_loss_val, loss_val = sess.run([reconstruction_loss, sparsity_loss, loss], feed_dict={X: X_batch})\n",
    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", reconstruction_loss_val, \"\\tSparsity loss:\", sparsity_loss_val, \"\\tTotal loss:\", loss_val)\n",
    "        saver.save(sess, \"./my_model_sparse.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_sparse.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c047c193c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_reconstructed_digits(X, outputs, \"./my_model_sparse.ckpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，编码层必须输出0到1的值，这就是我们使用sigmoid激活函数的原因："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.sigmoid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "一个简单的技巧可以加速收敛：\n",
    "\n",
    "我们可以**选择具有更大梯度的重建损失，而不是使用MSE**。 交叉熵通常是一个不错的选择。要使用它，我们必须对输入进行标准化，使它们采用从0到1的值，并**在输出层中使用逻辑激活函数**，这样输出也会采用0到1之间的值。\n",
    "\n",
    "TensorFlow的`sigmoid_cross_entropy_with_logits（）`函数负责将`logistic（sigmoid）`激活函数有效地应用于输出并计算交叉熵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits = tf.layers.dense(hidden1, n_outputs)\n",
    "outputs = tf.nn.sigmoid(logits)\n",
    "\n",
    "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits)\n",
    "reconstruction_loss = tf.reduce_mean(xentropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，训练期间不需要输出操作（我们只在想要查看重建时使用它）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Variational Autoencoder - 变分自动编码器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一个重要的自动编码器类别是由Diederik Kingma和Max Welling在2014年推出的，并迅速成为最受欢迎的自动编码器类型之一：**变分自动编码器**。它们与我们到目前为止讨论的所有自动编码器完全不同，\n",
    "\n",
    "特别是：\n",
    "\n",
    "* 它们是**概率自动编码器**，意味着即使在训练之后，它们的输出也部分地由机会确定（与仅在训练期间使用随机性的自动编码器去噪相反）。\n",
    "\n",
    "* 最重要的是，它们是**生成自动编码器**，这意味着它们可以生成看起来像是从训练集中采样的新实例。\n",
    "\n",
    "\n",
    "这两个属性使它们与RBM非常相似（见附录E），但它们更容易训练，采样过程更快（在使用RBM之前，你需要等待网络稳定到“热平衡”，然后才能对新实例进行采样）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来看看它们是如何工作的。 图15-11（左）显示了一个变分自动编码器。 当然，你可以识别所有自动编码器的基本结构，编码器后是解码器（在本例中，它们都有两个隐藏层），但有一个转折：编码器产生平均编码$μ$和标准偏差$σ$，而不是直接为给定的输入生成编码 。\n",
    "\n",
    "然后从具有均值 $μ$ 和标准差 $σ$ 的高斯分布中随机地采样实际编码。之后，解码器正常解码采样编码。该图的右侧部分显示了通过此自动编码器的训练实例。\n",
    "\n",
    "* 首先，编码器产生 $μ$ 和$σ$，\n",
    "* 然后随机采样编码（注意它不是精确地位于 $μ$ ），\n",
    "* 最后，这个编码被解码，最终输出类似于训练实例。"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如图所示，尽管输入可能具有非常复杂的分布，但是变分自动编码器倾向于产生看起来好像是从简单的高斯分布中采样的编码：\n",
    "\n",
    "* 在训练期间，成本函数（下面讨论）推动编码在编码空间（也称为潜在空间）内逐渐迁移，以占据看起来像高斯点云的粗略（超）球形区域。\n",
    "\n",
    "* 一个重要的结果是，在训练变分自动编码器之后，你可以非常轻松地生成一个新实例：\n",
    "   * 只需从高斯分布中对随机编码进行采样，对其进行解码，\n",
    "   * 然后再进行操作！\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 500\n",
    "n_hidden2 = 500\n",
    "n_hidden3 = 20  # codings\n",
    "n_hidden4 = n_hidden2\n",
    "n_hidden5 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "learning_rate = 0.001\n",
    "\n",
    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
    "\n",
    "my_dense_layer = partial(\n",
    "    tf.layers.dense,\n",
    "    activation=tf.nn.elu,\n",
    "    kernel_initializer=initializer)\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "hidden1 = my_dense_layer(X, n_hidden1)\n",
    "hidden2 = my_dense_layer(hidden1, n_hidden2)\n",
    "hidden3_mean = my_dense_layer(hidden2, n_hidden3, activation=None)\n",
    "hidden3_sigma = my_dense_layer(hidden2, n_hidden3, activation=None)\n",
    "noise = tf.random_normal(tf.shape(hidden3_sigma), dtype=tf.float32)\n",
    "hidden3 = hidden3_mean + hidden3_sigma * noise\n",
    "hidden4 = my_dense_layer(hidden3, n_hidden4)\n",
    "hidden5 = my_dense_layer(hidden4, n_hidden5)\n",
    "logits = my_dense_layer(hidden5, n_outputs, activation=None)\n",
    "outputs = tf.sigmoid(logits)\n",
    "\n",
    "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits)\n",
    "reconstruction_loss = tf.reduce_sum(xentropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "那么让我们来看看成本函数。 它由两部分组成：\n",
    "\n",
    "* 第一部分是通常的重建损失，它推动自动编码器重现其输入（我们可以使用交叉熵，如前所述）。\n",
    "\n",
    "* 第二部分是**潜在的损失-latent loss**，它推动自动编码器具有看起来好像是从简单的高斯分布中采样的编码，为此我们使用目标分布（高斯分布）和编码的实际分布之间的KL偏差。\n",
    "\n",
    "从数学角度看比以前复杂得多，特别是因为高斯噪声限制了可以传输到编码层的信息量（从而推动自动编码器学习有用的特征）。\n",
    "\n",
    "幸运的是，方程简化了下面的潜在损失代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = 1e-10 # smoothing term to avoid computing log(0) which is NaN\n",
    "latent_loss = 0.5 * tf.reduce_sum(\n",
    "    tf.square(hidden3_sigma) + tf.square(hidden3_mean)\n",
    "    - 1 - tf.log(eps + tf.square(hidden3_sigma)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = reconstruction_loss + latent_loss\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一种常见的变体是训练编码器输出$γ= log(σ^2)$而不是 $σ$。只要我们需要 $σ$，我们都可以计算 $σ=exp(\\frac{γ}{2})$。这使得编码器更容易捕获不同尺度的sigma，因此它有助于加速收敛。 潜在的损失最终变得更简单了：\n",
    "```python\n",
    "latent_loss = 0.5 * tf.reduce_sum(\n",
    "    tf.exp(hidden3_gamma) + tf.square(hidden3_mean) - 1 - hidden3_gamma)\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09% Train total loss: 33956.375 \tReconstruction loss: 24482.105 \tLatent loss: 9474.271\n",
      "1 Train total loss: 30793.75 \tReconstruction loss: 25403.44 \tLatent loss: 5390.31\n",
      "29% Train total loss: 32093.91 \tReconstruction loss: 23220.719 \tLatent loss: 8873.19\n",
      "3 Train total loss: 34110.367 \tReconstruction loss: 25802.898 \tLatent loss: 8307.468\n",
      "4 Train total loss: 27073.686 \tReconstruction loss: 21987.027 \tLatent loss: 5086.6577\n",
      "5 Train total loss: 23463.988 \tReconstruction loss: 20248.355 \tLatent loss: 3215.6328\n",
      "69% Train total loss: 18757.344 \tReconstruction loss: 15888.006 \tLatent loss: 2869.3381\n",
      "7 Train total loss: 19263.246 \tReconstruction loss: 16365.022 \tLatent loss: 2898.223\n",
      "8 Train total loss: 16830.525 \tReconstruction loss: 13818.645 \tLatent loss: 3011.8813\n",
      "9 Train total loss: 17147.424 \tReconstruction loss: 14011.476 \tLatent loss: 3135.9487\n",
      "10 Train total loss: 16755.68 \tReconstruction loss: 13615.654 \tLatent loss: 3140.0251\n",
      "11 Train total loss: 16194.092 \tReconstruction loss: 13117.13 \tLatent loss: 3076.962\n",
      "12 Train total loss: 16463.086 \tReconstruction loss: 13212.588 \tLatent loss: 3250.4976\n",
      "13 Train total loss: 16259.547 \tReconstruction loss: 13128.701 \tLatent loss: 3130.8455\n",
      "14 Train total loss: 16457.016 \tReconstruction loss: 13135.972 \tLatent loss: 3321.045\n",
      "15% Train total loss: 16086.873 \tReconstruction loss: 12976.621 \tLatent loss: 3110.2517\n",
      "16 Train total loss: 15418.547 \tReconstruction loss: 12161.35 \tLatent loss: 3257.1973\n",
      "17 Train total loss: 17659.277 \tReconstruction loss: 14092.964 \tLatent loss: 3566.3145\n",
      "18% Train total loss: 17122.902 \tReconstruction loss: 13973.111 \tLatent loss: 3149.7913\n",
      "19 Train total loss: 18580.656 \tReconstruction loss: 15155.241 \tLatent loss: 3425.4143\n",
      "20 Train total loss: 21947.592 \tReconstruction loss: 17679.11 \tLatent loss: 4268.4824\n",
      "21 Train total loss: 19404.252 \tReconstruction loss: 15825.979 \tLatent loss: 3578.2727\n",
      "22 Train total loss: 16194.604 \tReconstruction loss: 13068.689 \tLatent loss: 3125.9146\n",
      "23 Train total loss: 16691.525 \tReconstruction loss: 13299.274 \tLatent loss: 3392.2507\n",
      "24 Train total loss: 15668.123 \tReconstruction loss: 12489.98 \tLatent loss: 3178.1423\n",
      "25 Train total loss: 15613.809 \tReconstruction loss: 12334.985 \tLatent loss: 3278.823\n",
      "26 Train total loss: 17087.654 \tReconstruction loss: 13611.602 \tLatent loss: 3476.053\n",
      "27 Train total loss: 16159.728 \tReconstruction loss: 12796.145 \tLatent loss: 3363.583\n",
      "28 Train total loss: 15391.068 \tReconstruction loss: 12031.66 \tLatent loss: 3359.4077\n",
      "29 Train total loss: 15579.858 \tReconstruction loss: 12082.496 \tLatent loss: 3497.362\n",
      "30 Train total loss: 15898.047 \tReconstruction loss: 12511.436 \tLatent loss: 3386.6108\n",
      "31 Train total loss: 21421.906 \tReconstruction loss: 17461.676 \tLatent loss: 3960.2312\n",
      "32 Train total loss: 29961.79 \tReconstruction loss: 22051.82 \tLatent loss: 7909.9683\n",
      "33 Train total loss: 28910.684 \tReconstruction loss: 21849.898 \tLatent loss: 7060.786\n",
      "34 Train total loss: 24108.928 \tReconstruction loss: 18639.562 \tLatent loss: 5469.3647\n",
      "35% Train total loss: 22020.477 \tReconstruction loss: 17703.715 \tLatent loss: 4316.7627\n",
      "36 Train total loss: 17460.105 \tReconstruction loss: 14240.742 \tLatent loss: 3219.3643\n",
      "37 Train total loss: 17195.31 \tReconstruction loss: 13862.962 \tLatent loss: 3332.348\n",
      "38 Train total loss: 16548.365 \tReconstruction loss: 13035.168 \tLatent loss: 3513.197\n",
      "39% Train total loss: 16160.247 \tReconstruction loss: 12759.598 \tLatent loss: 3400.6492\n",
      "40% Train total loss: 15401.363 \tReconstruction loss: 12074.738 \tLatent loss: 3326.6245\n",
      "41 Train total loss: 15487.709 \tReconstruction loss: 12168.111 \tLatent loss: 3319.5977\n",
      "42 Train total loss: 15876.186 \tReconstruction loss: 12427.244 \tLatent loss: 3448.941\n",
      "43 Train total loss: 15220.738 \tReconstruction loss: 11830.164 \tLatent loss: 3390.5745\n",
      "44 Train total loss: 15497.828 \tReconstruction loss: 12097.248 \tLatent loss: 3400.5798\n",
      "45 Train total loss: 17852.793 \tReconstruction loss: 14241.443 \tLatent loss: 3611.3486\n",
      "46 Train total loss: 22391.768 \tReconstruction loss: 17675.652 \tLatent loss: 4716.115\n",
      "47% Train total loss: 22262.602 \tReconstruction loss: 18912.621 \tLatent loss: 3349.9795\n",
      "48 Train total loss: 15147.828 \tReconstruction loss: 11886.143 \tLatent loss: 3261.6853\n",
      "49% Train total loss: 31211.121 \tReconstruction loss: 23668.691 \tLatent loss: 7542.4297\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 50\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch})\n",
    "        loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch})\n",
    "        print(\"\\r{}\".format(epoch), \"Train total loss:\", loss_val, \"\\tReconstruction loss:\", reconstruction_loss_val, \"\\tLatent loss:\", latent_loss_val)\n",
    "        saver.save(sess, \"./my_model_variational.ckpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下代码构建了如图15-11所示的变分自动编码器（左），使用$log(σ^2)$变体："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 500\n",
    "n_hidden2 = 500\n",
    "n_hidden3 = 20  # codings\n",
    "n_hidden4 = n_hidden2\n",
    "n_hidden5 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "learning_rate = 0.001\n",
    "\n",
    "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
    "my_dense_layer = partial(\n",
    "    tf.layers.dense,\n",
    "    activation=tf.nn.elu,\n",
    "    kernel_initializer=initializer)\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "hidden1 = my_dense_layer(X, n_hidden1)\n",
    "hidden2 = my_dense_layer(hidden1, n_hidden2)\n",
    "hidden3_mean = my_dense_layer(hidden2, n_hidden3, activation=None)\n",
    "hidden3_gamma = my_dense_layer(hidden2, n_hidden3, activation=None)\n",
    "noise = tf.random_normal(tf.shape(hidden3_gamma), dtype=tf.float32)\n",
    "hidden3 = hidden3_mean + tf.exp(0.5 * hidden3_gamma) * noise\n",
    "hidden4 = my_dense_layer(hidden3, n_hidden4)\n",
    "hidden5 = my_dense_layer(hidden4, n_hidden5)\n",
    "logits = my_dense_layer(hidden5, n_outputs, activation=None)\n",
    "outputs = tf.sigmoid(logits)\n",
    "\n",
    "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=X, logits=logits)\n",
    "reconstruction_loss = tf.reduce_sum(xentropy)\n",
    "latent_loss = 0.5 * tf.reduce_sum(\n",
    "    tf.exp(hidden3_gamma) + tf.square(hidden3_mean) - 1 - hidden3_gamma)\n",
    "loss = reconstruction_loss + latent_loss\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 Generate digits - 生成数字\n",
    "\n",
    "现在让我们使用这个变分自动编码器来生成看起来像手写数字的图像。 我们需要做的就是训练模型，然后从高斯分布中抽样随机编码并对其进行解码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "09% Train total loss: 17792.723 \tReconstruction loss: 14123.083 \tLatent loss: 3669.6404\n",
      "1 Train total loss: 17332.346 \tReconstruction loss: 13560.1 \tLatent loss: 3772.2463\n",
      "29% Train total loss: 16350.873 \tReconstruction loss: 12579.393 \tLatent loss: 3771.4807\n",
      "3 Train total loss: 16581.447 \tReconstruction loss: 12810.661 \tLatent loss: 3770.7866\n",
      "4 Train total loss: 16224.016 \tReconstruction loss: 12450.172 \tLatent loss: 3773.8433\n",
      "5 Train total loss: 15628.262 \tReconstruction loss: 11819.755 \tLatent loss: 3808.5068\n",
      "6 Train total loss: 16081.008 \tReconstruction loss: 12179.777 \tLatent loss: 3901.231\n",
      "79% Train total loss: 15772.934 \tReconstruction loss: 12021.412 \tLatent loss: 3751.5212\n",
      "89% Train total loss: 16276.585 \tReconstruction loss: 12404.786 \tLatent loss: 3871.7988\n",
      "9 Train total loss: 15589.723 \tReconstruction loss: 11740.717 \tLatent loss: 3849.0063\n",
      "10 Train total loss: 15931.363 \tReconstruction loss: 12031.385 \tLatent loss: 3899.9783\n",
      "11% Train total loss: 16112.865 \tReconstruction loss: 12238.496 \tLatent loss: 3874.369\n",
      "12% Train total loss: 16002.043 \tReconstruction loss: 12185.183 \tLatent loss: 3816.8608\n",
      "13 Train total loss: 15357.919 \tReconstruction loss: 11667.58 \tLatent loss: 3690.3389\n",
      "14 Train total loss: 16208.518 \tReconstruction loss: 12264.616 \tLatent loss: 3943.9011\n",
      "15 Train total loss: 15970.236 \tReconstruction loss: 12158.714 \tLatent loss: 3811.522\n",
      "16% Train total loss: 15551.721 \tReconstruction loss: 11783.23 \tLatent loss: 3768.4897\n",
      "17 Train total loss: 15330.088 \tReconstruction loss: 11555.771 \tLatent loss: 3774.317\n",
      "18 Train total loss: 15251.499 \tReconstruction loss: 11584.66 \tLatent loss: 3666.8386\n",
      "19 Train total loss: 15196.146 \tReconstruction loss: 11516.704 \tLatent loss: 3679.4424\n",
      "20 Train total loss: 15324.053 \tReconstruction loss: 11526.015 \tLatent loss: 3798.0386\n",
      "21% Train total loss: 15358.613 \tReconstruction loss: 11515.41 \tLatent loss: 3843.2036\n",
      "22 Train total loss: 15298.06 \tReconstruction loss: 11582.691 \tLatent loss: 3715.3684\n",
      "23 Train total loss: 14673.169 \tReconstruction loss: 10940.84 \tLatent loss: 3732.3293\n",
      "24 Train total loss: 15293.576 \tReconstruction loss: 11561.807 \tLatent loss: 3731.7695\n",
      "25 Train total loss: 15256.209 \tReconstruction loss: 11540.664 \tLatent loss: 3715.545\n",
      "26 Train total loss: 15305.479 \tReconstruction loss: 11475.454 \tLatent loss: 3830.0247\n",
      "27% Train total loss: 15276.918 \tReconstruction loss: 11449.658 \tLatent loss: 3827.2593\n",
      "28 Train total loss: 14980.705 \tReconstruction loss: 11318.064 \tLatent loss: 3662.6406\n",
      "29 Train total loss: 15232.897 \tReconstruction loss: 11520.201 \tLatent loss: 3712.6965\n",
      "30 Train total loss: 14872.444 \tReconstruction loss: 11173.002 \tLatent loss: 3699.4421\n",
      "31 Train total loss: 14890.485 \tReconstruction loss: 11144.245 \tLatent loss: 3746.2402\n",
      "32 Train total loss: 15246.843 \tReconstruction loss: 11439.51 \tLatent loss: 3807.3333\n",
      "33 Train total loss: 15063.674 \tReconstruction loss: 11282.296 \tLatent loss: 3781.3784\n",
      "34 Train total loss: 15046.693 \tReconstruction loss: 11310.135 \tLatent loss: 3736.5586\n",
      "35 Train total loss: 15293.909 \tReconstruction loss: 11599.4375 \tLatent loss: 3694.472\n",
      "36 Train total loss: 15134.561 \tReconstruction loss: 11362.827 \tLatent loss: 3771.7336\n",
      "37 Train total loss: 14705.801 \tReconstruction loss: 11054.809 \tLatent loss: 3650.9924\n",
      "38 Train total loss: 14914.009 \tReconstruction loss: 11077.051 \tLatent loss: 3836.958\n",
      "39 Train total loss: 14848.272 \tReconstruction loss: 11198.791 \tLatent loss: 3649.4814\n",
      "40% Train total loss: 14694.52 \tReconstruction loss: 10991.713 \tLatent loss: 3702.807\n",
      "41 Train total loss: 15223.751 \tReconstruction loss: 11464.885 \tLatent loss: 3758.8662\n",
      "42 Train total loss: 14585.404 \tReconstruction loss: 11019.355 \tLatent loss: 3566.0483\n",
      "43 Train total loss: 14579.119 \tReconstruction loss: 10931.234 \tLatent loss: 3647.885\n",
      "44 Train total loss: 15049.284 \tReconstruction loss: 11381.961 \tLatent loss: 3667.3232\n",
      "45% Train total loss: 14855.67 \tReconstruction loss: 11125.5205 \tLatent loss: 3730.1492\n",
      "46 Train total loss: 14777.61 \tReconstruction loss: 11093.045 \tLatent loss: 3684.5652\n",
      "47 Train total loss: 14408.994 \tReconstruction loss: 10788.754 \tLatent loss: 3620.2405\n",
      "48 Train total loss: 14479.197 \tReconstruction loss: 10864.229 \tLatent loss: 3614.9683\n",
      "49 Train total loss: 14638.091 \tReconstruction loss: 10926.593 \tLatent loss: 3711.498\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "n_digits = 60\n",
    "n_epochs = 50\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # not shown in the book\n",
    "            sys.stdout.flush()                                          # not shown\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch})\n",
    "        loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch}) # not shown\n",
    "        print(\"\\r{}\".format(epoch), \"Train total loss:\", loss_val, \"\\tReconstruction loss:\", reconstruction_loss_val, \"\\tLatent loss:\", latent_loss_val)  # not shown\n",
    "        saver.save(sess, \"./my_model_variational.ckpt\")  # not shown\n",
    "    \n",
    "    codings_rnd = np.random.normal(size=[n_digits, n_hidden3])\n",
    "    outputs_val = outputs.eval(feed_dict={hidden3: codings_rnd})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以看到自动编码器产生的“手写”数字是什么样的（见图15-12）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04b28d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,50)) # not shown in the book\n",
    "for iteration in range(n_digits):\n",
    "    plt.subplot(n_digits, 10, iteration + 1)\n",
    "    plot_image(outputs_val[iteration])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这些数字中的大多数看起来非常有说服力，而有些数字则相当“富有创意”。但是对自动编码器不要太苛刻——它只在不到一个小时前才开始学习。 给它一点训练时间，这些数字看起来会越来越好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure generated_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04b896320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_rows = 6\n",
    "n_cols = 10\n",
    "plot_multiple_images(outputs_val.reshape(-1, 28, 28), n_rows, n_cols)\n",
    "save_fig(\"generated_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，在第二个变体中，潜在损失的计算方式不同："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_loss = 0.5 * tf.reduce_sum(\n",
    "    tf.exp(hidden3_gamma) + tf.square(hidden3_mean) - 1 - hidden3_gamma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Other Autoencoders - 其他自动编码器\n",
    "\n",
    "监督学习在图像识别，语音识别，文本翻译等方面取得的惊人成功有点掩盖了无监督学习，但它实际上正在蓬勃发展。\n",
    "\n",
    "自动编码器和其他无监督学习算法的新架构是定期发明的，因此我们无法在本书中全部介绍它们。 以下是你可能想要查看的更多类型的自动编码器的简要概述（绝不是详尽无遗）：\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 压缩自动编码器（CAE）\n",
    "\n",
    "自动编码器在训练期间受到约束，因此关于输入的编码的导数很小。 换句话说，两个相似的输入必须具有相似的编码。\n",
    "\n",
    "* 堆叠卷积自动编码器\n",
    "\n",
    "自动编码器，学习通过重建通过卷积层处理的图像来提取视觉特征。\n",
    "\n",
    "\n",
    "* 生成随机网络（GSN）10\n",
    "消除自动编码器的一般化，增加了生成数据的能力。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Winner-take-all（WTA）自动编码器\n",
    "\n",
    "在训练期间，在计算编码层中所有神经元的激活之后，仅保留训练批次上的每个神经元的顶部 $k$ 激活，并且其余的被设置为零。 当然，这导致稀疏编码。此外， 类似的WTA方法可用于产生稀疏卷积自动编码器。\n",
    "\n",
    "* 对抗性自动编码器\n",
    "\n",
    "训练一个网络以再现其输入，同时训练另一个网络以找到第一网络无法正确重建的输入。这推动了第一个自动编码器学习强大的编码。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Additional material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.1 Encode & Decode - 编码和解码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_variational.ckpt\n"
     ]
    }
   ],
   "source": [
    "n_digits = 3\n",
    "X_test, y_test = mnist.test.next_batch(batch_size)\n",
    "codings = hidden3\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_model_variational.ckpt\")\n",
    "    codings_val = codings.eval(feed_dict={X: X_test})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_variational.ckpt\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_model_variational.ckpt\")\n",
    "    outputs_val = outputs.eval(feed_dict={codings: codings_val})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们绘制重建图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c05176d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 2.5 * n_digits))\n",
    "for iteration in range(n_digits):\n",
    "    plt.subplot(n_digits, 2, 1 + 2 * iteration)\n",
    "    plot_image(X_test[iteration])\n",
    "    plt.subplot(n_digits, 2, 2 + 2 * iteration)\n",
    "    plot_image(outputs_val[iteration])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2 Interpolate digits -  插值数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_model_variational.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c05361cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04c237278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04af55c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c04ba17d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "n_iterations = 3\n",
    "n_digits = 6\n",
    "codings_rnd = np.random.normal(size=[n_digits, n_hidden3])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_model_variational.ckpt\")\n",
    "    target_codings = np.roll(codings_rnd, -1, axis=0)\n",
    "    for iteration in range(n_iterations + 1):\n",
    "        codings_interpolate = codings_rnd + (target_codings - codings_rnd) * iteration / n_iterations\n",
    "        outputs_val = outputs.eval(feed_dict={codings: codings_interpolate})\n",
    "        plt.figure(figsize=(11, 1.5*n_iterations))\n",
    "        for digit_index in range(n_digits):\n",
    "            plt.subplot(1, n_digits, digit_index + 1)\n",
    "            plot_image(outputs_val[digit_index])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1 to 7**\n",
    "\n",
    "请移步我的简书[Chapter -15 Exercise(1-7)](https://www.jianshu.com/p/4f7e49a47426)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coming soon..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  },
  "nav_menu": {
   "height": "381px",
   "width": "453px"
  },
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
